Speedup_Notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZoMavn_hBno"
      },
      "source": [
        "# Installing dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "05uydQGB6z3N"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install git+https://github.com/nickkunz/smogn.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VB0iDa0-hKsB"
      },
      "source": [
        "# Importing libraries and frameworks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8eqwMajUuskD"
      },
      "outputs": [],
      "source": [
        "# General python libraries to arrange dataset and plot graphs\n",
        "import re\n",
        "import smogn\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy import cov\n",
        "from numpy import loadtxt\n",
        "from matplotlib import pyplot\n",
        "\n",
        "# Data pre-processing and splitting\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Downloading images\n",
        "from google.colab import files\n",
        "\n",
        "# Measure of accuracy\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import mean_absolute_error as mae\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.preprocessing import scale \n",
        "\n",
        "# Importing the Random Forest libraries for regression and feature selection\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Importing the Linear Regression libraries\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression, Lasso, LassoCV, Ridge, RidgeCV\n",
        "\n",
        "# importing SVM Libraries\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "# ANN for regression\n",
        "import tensorflow as tf\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Flatten"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_Ro480ch7sS"
      },
      "source": [
        "# Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "RV7Uk3s9vHhK"
      },
      "outputs": [],
      "source": [
        "# Import dataset\n",
        "df = pd.read_csv(\"/content/sample_data/dataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "h2KYeP4i8G-2",
        "outputId": "72de2c76-eef2-4eae-8211-1e7a0bc4354c"
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Threads</th>\n",
              "      <th>Size</th>\n",
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              "      <th>branch-misses in %</th>\n",
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              "      <th>LLC-load-misses</th>\n",
              "      <th>Exe-time</th>\n",
              "      <th>Speedup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>matrix_multiplication</td>\n",
              "      <td>1</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>143684436</td>\n",
              "      <td>0.21</td>\n",
              "      <td>65664709</td>\n",
              "      <td>48.300</td>\n",
              "      <td>135952510</td>\n",
              "      <td>9.253340e+09</td>\n",
              "      <td>6.198231e+09</td>\n",
              "      <td>0.67</td>\n",
              "      <td>3943.67</td>\n",
              "      <td>2413</td>\n",
              "      <td>3.284836e+09</td>\n",
              "      <td>50053</td>\n",
              "      <td>65551272</td>\n",
              "      <td>3.767890</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>matrix_multiplication</td>\n",
              "      <td>2</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>279763702</td>\n",
              "      <td>0.11</td>\n",
              "      <td>65929676</td>\n",
              "      <td>48.569</td>\n",
              "      <td>135743873</td>\n",
              "      <td>8.216107e+09</td>\n",
              "      <td>6.355125e+09</td>\n",
              "      <td>0.77</td>\n",
              "      <td>3519.53</td>\n",
              "      <td>2772</td>\n",
              "      <td>3.159218e+09</td>\n",
              "      <td>33541</td>\n",
              "      <td>65901746</td>\n",
              "      <td>1.844048</td>\n",
              "      <td>2.043271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>matrix_multiplication</td>\n",
              "      <td>4</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>283327885</td>\n",
              "      <td>0.10</td>\n",
              "      <td>67158355</td>\n",
              "      <td>49.254</td>\n",
              "      <td>136349806</td>\n",
              "      <td>8.644179e+09</td>\n",
              "      <td>6.353836e+09</td>\n",
              "      <td>0.74</td>\n",
              "      <td>3702.91</td>\n",
              "      <td>1776</td>\n",
              "      <td>3.177617e+09</td>\n",
              "      <td>10434</td>\n",
              "      <td>66861668</td>\n",
              "      <td>0.990931</td>\n",
              "      <td>3.802374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>matrix_multiplication</td>\n",
              "      <td>8</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>287638885</td>\n",
              "      <td>0.10</td>\n",
              "      <td>73962101</td>\n",
              "      <td>54.350</td>\n",
              "      <td>136084681</td>\n",
              "      <td>8.914081e+09</td>\n",
              "      <td>6.393717e+09</td>\n",
              "      <td>0.72</td>\n",
              "      <td>3806.00</td>\n",
              "      <td>1788</td>\n",
              "      <td>3.218620e+09</td>\n",
              "      <td>10216</td>\n",
              "      <td>73898040</td>\n",
              "      <td>0.552178</td>\n",
              "      <td>6.823685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>matrix_multiplication</td>\n",
              "      <td>16</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>300239815</td>\n",
              "      <td>0.10</td>\n",
              "      <td>76271406</td>\n",
              "      <td>56.012</td>\n",
              "      <td>136169963</td>\n",
              "      <td>9.009864e+09</td>\n",
              "      <td>6.419541e+09</td>\n",
              "      <td>0.71</td>\n",
              "      <td>3854.19</td>\n",
              "      <td>1813</td>\n",
              "      <td>3.230948e+09</td>\n",
              "      <td>40422</td>\n",
              "      <td>76265372</td>\n",
              "      <td>0.271104</td>\n",
              "      <td>13.898327</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                    Name  Threads  ...  Exe-time    Speedup\n",
              "0  matrix_multiplication        1  ...  3.767890   1.000000\n",
              "1  matrix_multiplication        2  ...  1.844048   2.043271\n",
              "2  matrix_multiplication        4  ...  0.990931   3.802374\n",
              "3  matrix_multiplication        8  ...  0.552178   6.823685\n",
              "4  matrix_multiplication       16  ...  0.271104  13.898327\n",
              "\n",
              "[5 rows x 18 columns]"
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        "df.head()"
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      "execution_count": 5,
      "metadata": {
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      "outputs": [],
      "source": [
        "df = df[df.columns[1:18]]"
      ]
    },
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      "execution_count": 6,
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              "      <td>33541</td>\n",
              "      <td>65901746</td>\n",
              "      <td>1.844048</td>\n",
              "      <td>2.043271</td>\n",
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              "      <th>2</th>\n",
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              "      <td>10434</td>\n",
              "      <td>66861668</td>\n",
              "      <td>0.990931</td>\n",
              "      <td>3.802374</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>287638885</td>\n",
              "      <td>0.10</td>\n",
              "      <td>73962101</td>\n",
              "      <td>54.350</td>\n",
              "      <td>136084681</td>\n",
              "      <td>8.914081e+09</td>\n",
              "      <td>6.393717e+09</td>\n",
              "      <td>0.72</td>\n",
              "      <td>3806.00</td>\n",
              "      <td>1788</td>\n",
              "      <td>3.218620e+09</td>\n",
              "      <td>10216</td>\n",
              "      <td>73898040</td>\n",
              "      <td>0.552178</td>\n",
              "      <td>6.823685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>simsmall</td>\n",
              "      <td>300239815</td>\n",
              "      <td>0.10</td>\n",
              "      <td>76271406</td>\n",
              "      <td>56.012</td>\n",
              "      <td>136169963</td>\n",
              "      <td>9.009864e+09</td>\n",
              "      <td>6.419541e+09</td>\n",
              "      <td>0.71</td>\n",
              "      <td>3854.19</td>\n",
              "      <td>1813</td>\n",
              "      <td>3.230948e+09</td>\n",
              "      <td>40422</td>\n",
              "      <td>76265372</td>\n",
              "      <td>0.271104</td>\n",
              "      <td>13.898327</td>\n",
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            "text/plain": [
              "   Threads      Size  branch-instructions  ...  LLC-load-misses  Exe-time    Speedup\n",
              "0        1  simsmall            143684436  ...         65551272  3.767890   1.000000\n",
              "1        2  simsmall            279763702  ...         65901746  1.844048   2.043271\n",
              "2        4  simsmall            283327885  ...         66861668  0.990931   3.802374\n",
              "3        8  simsmall            287638885  ...         73898040  0.552178   6.823685\n",
              "4       16  simsmall            300239815  ...         76265372  0.271104  13.898327\n",
              "\n",
              "[5 rows x 17 columns]"
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>8.216107e+09</td>\n",
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              "      <td>0.77</td>\n",
              "      <td>3519.53</td>\n",
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              "      <td>3.159218e+09</td>\n",
              "      <td>33541</td>\n",
              "      <td>65901746</td>\n",
              "      <td>1.844048</td>\n",
              "      <td>2.043271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>10434</td>\n",
              "      <td>66861668</td>\n",
              "      <td>0.990931</td>\n",
              "      <td>3.802374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>287638885</td>\n",
              "      <td>0.10</td>\n",
              "      <td>73962101</td>\n",
              "      <td>54.350</td>\n",
              "      <td>136084681</td>\n",
              "      <td>8.914081e+09</td>\n",
              "      <td>6.393717e+09</td>\n",
              "      <td>0.72</td>\n",
              "      <td>3806.00</td>\n",
              "      <td>1788</td>\n",
              "      <td>3.218620e+09</td>\n",
              "      <td>10216</td>\n",
              "      <td>73898040</td>\n",
              "      <td>0.552178</td>\n",
              "      <td>6.823685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>2.0</td>\n",
              "      <td>300239815</td>\n",
              "      <td>0.10</td>\n",
              "      <td>76271406</td>\n",
              "      <td>56.012</td>\n",
              "      <td>136169963</td>\n",
              "      <td>9.009864e+09</td>\n",
              "      <td>6.419541e+09</td>\n",
              "      <td>0.71</td>\n",
              "      <td>3854.19</td>\n",
              "      <td>1813</td>\n",
              "      <td>3.230948e+09</td>\n",
              "      <td>40422</td>\n",
              "      <td>76265372</td>\n",
              "      <td>0.271104</td>\n",
              "      <td>13.898327</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Threads  Size  branch-instructions  ...  LLC-load-misses  Exe-time    Speedup\n",
              "0        1   2.0            143684436  ...         65551272  3.767890   1.000000\n",
              "1        2   2.0            279763702  ...         65901746  1.844048   2.043271\n",
              "2        4   2.0            283327885  ...         66861668  0.990931   3.802374\n",
              "3        8   2.0            287638885  ...         73898040  0.552178   6.823685\n",
              "4       16   2.0            300239815  ...         76265372  0.271104  13.898327\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"Size\"] = OrdinalEncoder().fit_transform(df[[\"Size\"]])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rt2Y-iVAg67h"
      },
      "source": [
        "# Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KdOMzEIXA40",
        "outputId": "d119a9db-eda9-4785-9141-a0dcc7c9ea4b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "dist_matrix: 100%|##########| 26/26 [00:00<00:00, 57.37it/s]\n",
            "synth_matrix: 100%|##########| 26/26 [00:00<00:00, 97.16it/s]\n",
            "dist_matrix: 100%|##########| 65/65 [00:02<00:00, 26.41it/s]\n",
            "r_index: 100%|##########| 40/40 [00:00<00:00, 351.03it/s]\n"
          ]
        }
      ],
      "source": [
        "## conduct smogn\n",
        "df_smogn = smogn.smoter(\n",
        "    data = df,  ## pandas dataframe\n",
        "    y = 'Speedup'  ## string ('header name')\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYTfI3HaXAfR",
        "outputId": "b0315092-7aea-428e-bedd-167f9e3ef359"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(316, 17)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBzMeHO4NSHK",
        "outputId": "dfc1c91e-afd8-4cde-80bd-b4e959e02152"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(329, 17)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_smogn.reset_index(drop=True, inplace=True)\n",
        "df_smogn.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ScvIv1xgNxlr",
        "outputId": "94d7f830-f6a8-4a7a-f7c9-c67c679cec3d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threads</th>\n",
              "      <th>Size</th>\n",
              "      <th>branch-instructions</th>\n",
              "      <th>branch-misses in %</th>\n",
              "      <th>L3-cache-misses</th>\n",
              "      <th>L3-cache-miss-rate in %</th>\n",
              "      <th>L3-cache-references</th>\n",
              "      <th>cpu-cycles</th>\n",
              "      <th>total-instructions</th>\n",
              "      <th>IPC</th>\n",
              "      <th>cpu-clock</th>\n",
              "      <th>page-faults</th>\n",
              "      <th>L1-data-cache-loads</th>\n",
              "      <th>L1-instruction-cache-load-misses</th>\n",
              "      <th>LLC-load-misses</th>\n",
              "      <th>Exe-time</th>\n",
              "      <th>Speedup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>1.881804</td>\n",
              "      <td>370235406</td>\n",
              "      <td>4.527045</td>\n",
              "      <td>9047670</td>\n",
              "      <td>10.449487</td>\n",
              "      <td>86562301</td>\n",
              "      <td>4.284279e+09</td>\n",
              "      <td>1.742331e+09</td>\n",
              "      <td>0.409409</td>\n",
              "      <td>1877.194947</td>\n",
              "      <td>135667</td>\n",
              "      <td>9.024817e+08</td>\n",
              "      <td>45235211</td>\n",
              "      <td>9083164</td>\n",
              "      <td>0.012118</td>\n",
              "      <td>5.964286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>363184542</td>\n",
              "      <td>4.643208</td>\n",
              "      <td>8872102</td>\n",
              "      <td>9.800576</td>\n",
              "      <td>90521796</td>\n",
              "      <td>3.978546e+09</td>\n",
              "      <td>1.783278e+09</td>\n",
              "      <td>0.449321</td>\n",
              "      <td>1730.301477</td>\n",
              "      <td>129820</td>\n",
              "      <td>9.230582e+08</td>\n",
              "      <td>48358184</td>\n",
              "      <td>9258039</td>\n",
              "      <td>0.010023</td>\n",
              "      <td>8.736364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>0.183782</td>\n",
              "      <td>386381749</td>\n",
              "      <td>4.413784</td>\n",
              "      <td>9746879</td>\n",
              "      <td>10.379151</td>\n",
              "      <td>93909804</td>\n",
              "      <td>4.270503e+09</td>\n",
              "      <td>1.763770e+09</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>1860.418295</td>\n",
              "      <td>135594</td>\n",
              "      <td>9.249878e+08</td>\n",
              "      <td>49103508</td>\n",
              "      <td>9107829</td>\n",
              "      <td>0.067935</td>\n",
              "      <td>9.425676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>101790609</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>138506</td>\n",
              "      <td>35.457418</td>\n",
              "      <td>402702</td>\n",
              "      <td>5.112790e+08</td>\n",
              "      <td>3.828358e+08</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>297.867287</td>\n",
              "      <td>745</td>\n",
              "      <td>1.618865e+08</td>\n",
              "      <td>81538</td>\n",
              "      <td>83345</td>\n",
              "      <td>4.403387</td>\n",
              "      <td>17.389727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1551134029</td>\n",
              "      <td>2.722528</td>\n",
              "      <td>9253721</td>\n",
              "      <td>5.136830</td>\n",
              "      <td>179880974</td>\n",
              "      <td>1.449018e+10</td>\n",
              "      <td>9.636727e+09</td>\n",
              "      <td>0.668482</td>\n",
              "      <td>6281.160895</td>\n",
              "      <td>136591</td>\n",
              "      <td>4.431493e+09</td>\n",
              "      <td>57932948</td>\n",
              "      <td>9114520</td>\n",
              "      <td>2.074745</td>\n",
              "      <td>4.908413</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Threads      Size  branch-instructions  ...  LLC-load-misses  Exe-time    Speedup\n",
              "0        7  1.881804            370235406  ...          9083164  0.012118   5.964286\n",
              "1        7  2.000000            363184542  ...          9258039  0.010023   8.736364\n",
              "2        7  0.183782            386381749  ...          9107829  0.067935   9.425676\n",
              "3        8  2.000000            101790609  ...            83345  4.403387  17.389727\n",
              "4        2  1.000000           1551134029  ...          9114520  2.074745   4.908413\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_smogn.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-1GDg8csNggA",
        "outputId": "d9f9d19b-916e-4435-8352-334caa1413ef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fecb8c6d690>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.kdeplot(df['Speedup'], fill=True, palette=\"crest\", label = \"Without SMOTE\", legend=True)\n",
        "sns.kdeplot(df_smogn['Speedup'], fill=True, palette=\"crest\", label = \"After SMOTE\", legend=True)\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oft1yZw27YKl"
      },
      "outputs": [],
      "source": [
        "# Split data into X(Conditional variables) and Y(Output variable)\n",
        "X = df.iloc[:, 0:16 ]\n",
        "X_smogn = df_smogn.iloc[:, 0:16]\n",
        "\n",
        "# Execution time is the output variable. If we can predict the exe-time, we can predict the speedup.\n",
        "Y = df.loc[:, ['Speedup']]\n",
        "Y_smogn = df_smogn.loc[:, ['Speedup']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0lQH6o8fAaRj",
        "outputId": "5cfd4fd1-4e10-4855-a5e3-d76b845bfc89"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threads</th>\n",
              "      <th>Size</th>\n",
              "      <th>branch-instructions</th>\n",
              "      <th>branch-misses in %</th>\n",
              "      <th>L3-cache-misses</th>\n",
              "      <th>L3-cache-miss-rate in %</th>\n",
              "      <th>L3-cache-references</th>\n",
              "      <th>cpu-cycles</th>\n",
              "      <th>total-instructions</th>\n",
              "      <th>IPC</th>\n",
              "      <th>cpu-clock</th>\n",
              "      <th>page-faults</th>\n",
              "      <th>L1-data-cache-loads</th>\n",
              "      <th>L1-instruction-cache-load-misses</th>\n",
              "      <th>LLC-load-misses</th>\n",
              "      <th>Exe-time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>1.881804</td>\n",
              "      <td>370235406</td>\n",
              "      <td>4.527045</td>\n",
              "      <td>9047670</td>\n",
              "      <td>10.449487</td>\n",
              "      <td>86562301</td>\n",
              "      <td>4.284279e+09</td>\n",
              "      <td>1.742331e+09</td>\n",
              "      <td>0.409409</td>\n",
              "      <td>1877.194947</td>\n",
              "      <td>135667</td>\n",
              "      <td>9.024817e+08</td>\n",
              "      <td>45235211</td>\n",
              "      <td>9083164</td>\n",
              "      <td>0.012118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>363184542</td>\n",
              "      <td>4.643208</td>\n",
              "      <td>8872102</td>\n",
              "      <td>9.800576</td>\n",
              "      <td>90521796</td>\n",
              "      <td>3.978546e+09</td>\n",
              "      <td>1.783278e+09</td>\n",
              "      <td>0.449321</td>\n",
              "      <td>1730.301477</td>\n",
              "      <td>129820</td>\n",
              "      <td>9.230582e+08</td>\n",
              "      <td>48358184</td>\n",
              "      <td>9258039</td>\n",
              "      <td>0.010023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>0.183782</td>\n",
              "      <td>386381749</td>\n",
              "      <td>4.413784</td>\n",
              "      <td>9746879</td>\n",
              "      <td>10.379151</td>\n",
              "      <td>93909804</td>\n",
              "      <td>4.270503e+09</td>\n",
              "      <td>1.763770e+09</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>1860.418295</td>\n",
              "      <td>135594</td>\n",
              "      <td>9.249878e+08</td>\n",
              "      <td>49103508</td>\n",
              "      <td>9107829</td>\n",
              "      <td>0.067935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>101790609</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>138506</td>\n",
              "      <td>35.457418</td>\n",
              "      <td>402702</td>\n",
              "      <td>5.112790e+08</td>\n",
              "      <td>3.828358e+08</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>297.867287</td>\n",
              "      <td>745</td>\n",
              "      <td>1.618865e+08</td>\n",
              "      <td>81538</td>\n",
              "      <td>83345</td>\n",
              "      <td>4.403387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1551134029</td>\n",
              "      <td>2.722528</td>\n",
              "      <td>9253721</td>\n",
              "      <td>5.136830</td>\n",
              "      <td>179880974</td>\n",
              "      <td>1.449018e+10</td>\n",
              "      <td>9.636727e+09</td>\n",
              "      <td>0.668482</td>\n",
              "      <td>6281.160895</td>\n",
              "      <td>136591</td>\n",
              "      <td>4.431493e+09</td>\n",
              "      <td>57932948</td>\n",
              "      <td>9114520</td>\n",
              "      <td>2.074745</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Threads      Size  ...  LLC-load-misses  Exe-time\n",
              "0        7  1.881804  ...          9083164  0.012118\n",
              "1        7  2.000000  ...          9258039  0.010023\n",
              "2        7  0.183782  ...          9107829  0.067935\n",
              "3        8  2.000000  ...            83345  4.403387\n",
              "4        2  1.000000  ...          9114520  2.074745\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ze0J4Q2zApNj",
        "outputId": "4b7d352e-750a-44df-8610-5e5ee6030b0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       5.964286\n",
              "1       8.736364\n",
              "2       9.425676\n",
              "3      17.389727\n",
              "4       4.908413\n",
              "         ...    \n",
              "324     0.792857\n",
              "325     0.402649\n",
              "326     0.344828\n",
              "327     0.826984\n",
              "328     0.406091\n",
              "Name: Speedup, Length: 329, dtype: float64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y['Speedup']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "dEzXYp1Bmqyx"
      },
      "outputs": [],
      "source": [
        "X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, test_size = 0.2, random_state = 5)\n",
        "X_train_smogn, X_valid_smogn, Y_train_smogn, Y_valid_smogn = train_test_split(X_smogn, Y_smogn, test_size = 0.2, random_state = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Kq3qYoTqvOvc",
        "outputId": "160e1c4e-92d3-411c-ac92-3cad5fd0ee28"
      },
      "outputs": [
        {
          "data": {
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threads</th>\n",
              "      <th>Size</th>\n",
              "      <th>branch-instructions</th>\n",
              "      <th>branch-misses in %</th>\n",
              "      <th>L3-cache-misses</th>\n",
              "      <th>L3-cache-miss-rate in %</th>\n",
              "      <th>L3-cache-references</th>\n",
              "      <th>cpu-cycles</th>\n",
              "      <th>total-instructions</th>\n",
              "      <th>IPC</th>\n",
              "      <th>cpu-clock</th>\n",
              "      <th>page-faults</th>\n",
              "      <th>L1-data-cache-loads</th>\n",
              "      <th>L1-instruction-cache-load-misses</th>\n",
              "      <th>LLC-load-misses</th>\n",
              "      <th>Exe-time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>327</th>\n",
              "      <td>7</td>\n",
              "      <td>1.024183</td>\n",
              "      <td>372749616</td>\n",
              "      <td>4.539033</td>\n",
              "      <td>9749467</td>\n",
              "      <td>10.563879</td>\n",
              "      <td>92273773</td>\n",
              "      <td>4.217544e+09</td>\n",
              "      <td>1.832561e+09</td>\n",
              "      <td>0.439033</td>\n",
              "      <td>1830.873678</td>\n",
              "      <td>136183</td>\n",
              "      <td>9.367349e+08</td>\n",
              "      <td>47623878</td>\n",
              "      <td>9235674</td>\n",
              "      <td>0.034589</td>\n",
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              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>641528431</td>\n",
              "      <td>3.550000</td>\n",
              "      <td>8461273</td>\n",
              "      <td>7.592000</td>\n",
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              "      <td>7.486504e+09</td>\n",
              "      <td>3.629922e+09</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>3249.580000</td>\n",
              "      <td>136406</td>\n",
              "      <td>1.754824e+09</td>\n",
              "      <td>46791548</td>\n",
              "      <td>7624619</td>\n",
              "      <td>0.736000</td>\n",
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              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>8</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>2.940000</td>\n",
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              "      <td>25.798000</td>\n",
              "      <td>57883453</td>\n",
              "      <td>2.844316e+10</td>\n",
              "      <td>3.244002e+10</td>\n",
              "      <td>1.140000</td>\n",
              "      <td>11995.135580</td>\n",
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              "      <td>13046561</td>\n",
              "      <td>3.664033</td>\n",
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              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>16</td>\n",
              "      <td>1.625606</td>\n",
              "      <td>109356978</td>\n",
              "      <td>2.013889</td>\n",
              "      <td>32018</td>\n",
              "      <td>0.700334</td>\n",
              "      <td>6160874</td>\n",
              "      <td>1.001145e+09</td>\n",
              "      <td>6.238561e+08</td>\n",
              "      <td>0.587439</td>\n",
              "      <td>463.819485</td>\n",
              "      <td>109</td>\n",
              "      <td>2.101422e+08</td>\n",
              "      <td>3984696</td>\n",
              "      <td>51548</td>\n",
              "      <td>0.126829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>8</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>375272050</td>\n",
              "      <td>4.230000</td>\n",
              "      <td>7575626</td>\n",
              "      <td>8.936000</td>\n",
              "      <td>84771736</td>\n",
              "      <td>4.072865e+09</td>\n",
              "      <td>1.735370e+09</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>1771.920000</td>\n",
              "      <td>130279</td>\n",
              "      <td>8.968208e+08</td>\n",
              "      <td>40297067</td>\n",
              "      <td>7480045</td>\n",
              "      <td>0.093000</td>\n",
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              "</div>"
            ],
            "text/plain": [
              "     Threads      Size  ...  LLC-load-misses  Exe-time\n",
              "327        7  1.024183  ...          9235674  0.034589\n",
              "223        2  2.000000  ...          7624619  0.736000\n",
              "123        8  0.000000  ...         13046561  3.664033\n",
              "261       16  1.625606  ...            51548  0.126829\n",
              "191        8  0.000000  ...          7480045  0.093000\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjdSikyrqPcQ",
        "outputId": "01d7a2f8-9095-4f5b-cb05-4684591f959c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score Validation:  0.2032427216431395\n",
            "Linear Regression validation MAE:  1.7178180955660325\n"
          ]
        }
      ],
      "source": [
        "linreg = LinearRegression().fit(X_train, Y_train)\n",
        "Y_pred = linreg.predict(X_valid)\n",
        "print('R-squared score Validation: ', r2_score(Y_valid, Y_pred))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid , Y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OORc1SsczgY0",
        "outputId": "afc51e74-9229-411b-f981-17021bac457d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score Validation:  0.23188206642899212\n",
            "Linear Regression validation MAE:  0.9623703369636062\n"
          ]
        }
      ],
      "source": [
        "linreg_smogn = LinearRegression().fit(X_train_smogn, Y_train_smogn)\n",
        "Y_pred_smogn = linreg_smogn.predict(X_valid_smogn)\n",
        "print('R-squared score Validation: ', r2_score(Y_valid_smogn, Y_pred_smogn))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid_smogn , Y_pred_smogn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMJk3XIvzg_d",
        "outputId": "2a1ce19b-0f99-476d-8165-d21aec666dfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  0.4468971089983089\n",
            "Linear Regression validation MAE:  1.6048599860342245\n"
          ]
        }
      ],
      "source": [
        "lassocv = LassoCV(cv = 10, max_iter = 10000, normalize = True).fit(X_train, Y_train)\n",
        "Y_pred = lassocv.predict(X_valid)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid, Y_pred))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid , Y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkUdz9ZaY-EB",
        "outputId": "6ff1e193-869a-4c5e-ca93-1979f4ce052e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  0.19734774823765877\n",
            "Linear Regression validation MAE:  0.9720777719026186\n"
          ]
        }
      ],
      "source": [
        "lassocv_smogn = LassoCV(cv = 10, max_iter = 10000, normalize = True).fit(X_train_smogn, Y_train_smogn)\n",
        "Y_pred_smogn = lassocv_smogn.predict(X_valid_smogn)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid_smogn, Y_pred_smogn))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid_smogn, Y_pred_smogn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMhm2XwGZ_ij",
        "outputId": "ee0dc4af-926c-49ce-e22a-80da27359722"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  0.2394946833279853\n",
            "Linear Regression validation MAE:  1.7829396469669472\n"
          ]
        }
      ],
      "source": [
        "ridgecv = RidgeCV(cv = 10, normalize = True).fit(X_train, Y_train)\n",
        "Y_pred = ridgecv.predict(X_valid)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid, Y_pred))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid , Y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-RmV4Z5Z_r8",
        "outputId": "74e9385d-b293-4919-c399-9c3fded3e141"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  0.15532157319164608\n",
            "Linear Regression validation MAE:  0.8967486976293354\n"
          ]
        }
      ],
      "source": [
        "ridgecv_smogn = RidgeCV(cv = 10, normalize = True).fit(X_train_smogn, Y_train_smogn)\n",
        "Y_pred_smogn = ridgecv_smogn.predict(X_valid_smogn)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid_smogn, Y_pred_smogn))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid_smogn, Y_pred_smogn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEudURopctgv",
        "outputId": "995b06af-a80f-4f09-aeb1-9ccb81a541be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  -5.540631868834507\n",
            "Linear Regression validation MAE:  7.181773931720818\n"
          ]
        }
      ],
      "source": [
        "svr = SVR(kernel='rbf', C = 10, epsilon = 10).fit(X_train, Y_train)\n",
        "Y_pred = svr.predict(X_valid)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid, Y_pred))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid , Y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpPSq_03ctlh",
        "outputId": "b9b47cdb-7182-4b03-dee4-4dbf50d632c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  -10.117175932519158\n",
            "Linear Regression validation MAE:  6.616465010109375\n"
          ]
        }
      ],
      "source": [
        "svr_smogn = SVR(kernel='rbf', C = 10, epsilon = 10).fit(X_train_smogn, Y_train_smogn)\n",
        "Y_pred_smogn = svr_smogn.predict(X_valid_smogn)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid_smogn, Y_pred_smogn))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid_smogn, Y_pred_smogn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqHtq68zdstF",
        "outputId": "a9932acf-2a3d-4388-8e85-045d921ad90d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  0.5858575712615045\n",
            "Linear Regression validation MAE:  1.0350493832523529\n"
          ]
        }
      ],
      "source": [
        "forest = RandomForestRegressor().fit(X_train, Y_train)\n",
        "Y_pred = forest.predict(X_valid)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid, Y_pred))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid , Y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKNFZgC8ds3M",
        "outputId": "7433cb38-a189-4780-e066-0dd1a7c37671"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared score (Validation):  0.5777225813665736\n",
            "Linear Regression validation MAE:  0.7270991590509374\n"
          ]
        }
      ],
      "source": [
        "forest_smogn = RandomForestRegressor().fit(X_train_smogn, Y_train_smogn)\n",
        "Y_pred_smogn = forest_smogn.predict(X_valid_smogn)\n",
        "print('R-squared score (Validation): ', r2_score(Y_valid_smogn, Y_pred_smogn))\n",
        "print('Linear Regression validation MAE: ', mae(Y_valid_smogn, Y_pred_smogn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "5nj5MNjboPEO",
        "outputId": "074c4ae1-c721-4562-cce4-3ffcfcbdaae4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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ZJwDnEByMSZJWS3kdDa6lEJL2Izg93YBeZnYQcEBuWCWRb4mA0Mdf5QtuOY7jLEOqKyDUIrgTUD5pxIOgBgWEkqiAOI+k1Qnd738lPNz7RYngdyUdGO0kaau438vMXoyxrWfTUAazYuI9O40gNdyFpY5We6DBwuSkgNDaK65Tjeodx3FSYYvrUm+1ig8HlE8h8aBC1KKAUJLBNBbnWYcg8JNzDn8dPw8Hhks6hzBh7x5gCnC5pN6E3oynY1o1OBG4Lc6NmAp0lTQNeMTMvqhSHY7jONmo4Tf8tLgTUAYlxIMaiPbE4+YWEErmG0vo0i90rilxnkJyve8CexRI/1GhtiTOzwQ2j/sjSQgTmdk+Jez+mNg34NBS9TiO47QI9bU76z8t7gQ4juM4TiW0gomB7gQ4juM4TiW4E+A4tcOp9Wtnsr+jvnIhmQ8HVS4iA9kFgJ6YfGMm+2lbn16x7RavXJWpbuqzTZqq/+KTTPaH7lYoblV6Rk34Q8W2dW9PzFT3Ueuemc1+p2xxuJ6YVPnvbtHoa5rOVIJ/nJtNMKwq1HAQoLS4E+A4juM4lVDDs/7T4ksEm4lcuN68tBNieN7Jkv4hKZsma+n6RyZDFC/Dem5eltfhOI5TM7SCOAHeE9Cy3GVmNwBI2he4kgKz8JcnzOxnLd0Gx3GcZqEVrA7wnoAWJAbiybEijaMRAiBpI0lPRaGel6OgTzdJT8fjaTHCXi7/kQpyvFMk3ZEoamcVFi46U0HsaKqkC4q04XxJt0UBofck/UjSZbHux2KAnyXSxyXEiE6RNCPWdU9MW1HSLQoiSa/krkXSZjFtcszfu8Jb7TiOU3Wsvj71Vqt4T0ALI+lE4BeESHi7Fsl2J3CJmd0vqTPBefsG+KGZfRkj/f1T0higDyHa3w5m9lle5MFCwkW7A72BgYSgP2Mk7RzDCufTC9gl1vEC8GMz+6Wk+4G9gQcSeYuJEZ0FbGhmCxNpZwPPmNlPY9pLkp4ihC6+2szulNSREDHQcRynNqhyT4CkPYCrCX/rbo6xY/LzHAScT3hpnGJmh2Wp03sCWhgzu87MegG/Ijy8GyCpO+Fhen/MvyCKDAn4XYyo9xQh2t9aBEfiLzGwUL4Q0QNmVm9mM2JegN3j9gpBvW8TglNQiEfNbBFBoKg98FhMn0bjIEZLxIjiDzvX6zEVuFPSEUBOjHt34CxJkwlBjjoD6xMcjf8n6VfABmb2dZF2OY7jND9VnBOgoOZ6HbAn4UXr0Pz5VbE39NfAjma2GSG8eibcCagd7gH2h6LiQPkcDqwBbGNmfYFPaChSVIhCwkUCfp8QB9rIzP4k6cTYhsmSvp20jwqIi2I0PygsUFRIjAhCj8F1hMiEEyStENvw40Qb1jez18zsLmBf4GvgEUmNekqSAkJPzn+rict3HMepIovr0m9NMxB4y8zeMbNvCM+E/fLyHAtcF/++YmafZr0EdwJakLwx7r2BN6GhOJCZzQVmSco5CJ2iyNDKwKdmtkjSLgRZXoBngAMVFf2aEiIiSBz/VEEyGEnrSFoz9lDkHsofVnBtjcSIoibBemb2LKHnY2WWyiyfLAWtYklbx8/vAO+Y2TDgQWDL/HqSAkK7dc22Vt9xHKcs6i391jTr0FBafVZMS7IxsLGk8ZL+GXtZM+FzApqPrpJmJY6vBDaQNARYBPwXOKqI7U+AGyVdGPMeSJgn8LcorDMR+BeAmU2XdDHwd0l1hG7+ocUaZWZPSNoUeCE+g+cBRwBZPcxCYkTtgT9LWpnw9j/MzL6Q9Fvgj8DUmP9dYB/gIOAnCiJHHwO/y9gmx3Gc6lHG0j9JxwHHJZJGmNmIMmtcgTBcOxhYFxgnaYsswmruBDQTZlZxr4uZvUnhSYPbF0grKA7UhHDR1YTJKKXacH4J+/MT+4MT2RqJEREmJuaX/TXQKGReIUElx3GcmqGMiYHxgV/qof8BDeXY141pSWYBL8a5We9KeoPgFExI3ZA8fDjAcRzHcSqgyksEJwC9JW0YV0MdQljFleQBQi9Absh1Y8Ik7IrxngDHcRzHqYTF1Vv/b2aLJZ1EmCPVHrglDu9eCEw0szHx3O6SZgB1wJlm9nmWemWtQADBcQB+1fPQTD/mGfVzK7Y9sG6VpjOV4Kezn81k/9Ja/TPZZxEBennLMzLVXWdqOlMJOrTL9of4xXYrZrLvt7jylavjOnbJVPcui+Znst9//r8y2T/cvfL4XT02+LLpTCVYMDfbO+yGU57M9sMD5p2xX+q/Od2ueDBzfcsC7wlwHMdxnErwsMG1iVysZ1nUtX+5dUnaV9JZZeRfI343r+aWRMb0BxOxChzHcWoCq7fUW63SKp2AItxlZlvEwDqXEZboLdeY2c9i9L+qECNWFWN/QhSr1JjZmEJhL0twKHADIWjGabFN/wO8UkmsAsdxnGVKdeMEtAhtxglwsZ4g1lOgrpmSLpX0MiHI0LGxfVMk/VVSV0k7ECL3XR57UnrF7TFJk2I7NylQ9lBJ18b9kZKGFbonCRYBXYFOQJ1CNMHTCE6b4zhObVFfn36rUdrUnAC5WE8xPjezftF2NTO7Ke5fBBxjZtfE633IzO6L554GTjCzNyVtC1xf4p4WvSd55++K23GEiII/B+6IWgmO4zi1RRVXB7QUbaYnAFysp8StGZXY3zy+2U8j6BNslp9ZIcTwDsBfFER/biQ84Jui0D1ZgpnNMbO9zaw/4f78D8F5uknSfZIaBUdSQjtg8lzXDnAcp/kws9RbrdKmnIAEbVasR9LjsZ6bE0V8ldgfCZxkZlsAFxS5znbAF4nr6GtmmzZxP4rdk2KcC1xMmCfwD0JI5fPzMyW1A/p2d+0Ax3GaEZ8TsPwgF+tZGehmZj+I9fysSHHdgY/ivIPDE+lz47nc/Ip3JR0Y65ekrcptd4nr6Q2sa2ZjCXME6glzOLItqnYcx6kmrcAJaK1zAlysp4hYT4qyzgVeBGbHz+4x/R7gJkmnAAcQHIThks4BOsTzUzJeR46LgbPj/t2E+Q9nAedVqXzHcZzM1PLSv7S0SifAxXqW0Eisp0BdPfOOhwPDC+QbT+MlgiVlLM1sJGF4oeQ9KWB3UGL/U8L8A8dxnNrCnQDHcRzHaZvYYncCHKdmGLAw2xSXf3Wo3HavLd/PVPegadkCP2aJ/Q/Z4//3m3pFJvss1M/JNpp20S4XZbI/ftqwim37vTMpU93tvt0oPEdZ7LlTwVAlqdliYuUhPBaNviZT3dPO/SiT/YaZrCPeE+A4TlunJR0Ax2lRlv8wAW1ndUClqO3oEHRSiJQ4WdLBy7o+x3Gc5Z3WoB3gPQGVcZeZ3QBBJIew+qDkJLlaQNIKZlYsaNDWADEOQjXKcxzHad14T0DbZDnUIbhD0njgDgWlvr9GuwmSdpS0JvBnYICWagNsI+nvCtoAj0vqEcsbK+mPkiYCpzaR71JJL0l6Q9KgmN5e0hUKOgdTJZ0c04uV06T+geM4Tktgiy31Vqt4T0CFaPnSIegD7GRmX0u6C7jKzP4haX3gcTPbVNLPgDPMbJ8YKOgOYD8zmx2HBy4GfhrL62hm/WO+v5fIt4KZDZS0F/AbYAhBF6An0NfMFktaNZZzTZFy0uofOI7jNCvWCnoC3AmoEDO7DrhO0mGEh3eD4EMqoEMQ0zsQdAh2JnQmpdYhAGZIKqRDANCN4BQUcgLGmNnXcX8I0CcGKwJYSTGCYYLvApsDT8Z87YHkVNxRKfONjp+TWKp3MAS4ITeMYGb/kbR5iXJy+gcP0FA0yXEcp2VxJ8AhRMobDkGHgDC2/iFQbHJdUodgkaSZZNMhuDGZMfZQHBsP94qfSW2AdsB2OackYdfgEJhuZgUDJCXKaypfrt11lP6tlSpnb2BngpjQ2ZK2SM5DkHQcoXeB41YayG5dXT/AcZzmoTX0BPicgArQ8q1D8ARwcuJaCk0EfB1YQ1G1T1IHSY3UBMvIl+RJ4HhJKySus2A5KqJ/kCwsKSDkDoDjOM1KfRlbjeI9AU3T2nQITiEMY0wlfP/jCMqDybK/iRMQhyloD6wA/BGYXkm+PG4GNgamSloE3GRm1xYp5w0q0z9wHMdZ5rSGngB3ApqgFeoQfEaBoYqo2Dc2cTyZ0A2fn29w3nGT+WKdPeP+YsKEyl+kKYcU+geO4zgtQX2VF0hL2oPwN709cLOZXVIk34+B+4ABZjYxS50+HOA4juM4lWBKvzWBpPbAdcCehBVdh6pAILo46fxUgsprZtwJcBzHcZwKsPr0WwoGAm+Z2Ttm9g1h0vl+BfL9FrgUWFDgXNn4cIDTaljBsgXkqCsc8ykVHdbOoD4ELJiasV+xvi6TeV2KN5VaRSt0zGTfVcvvn0G1z9b2rrTP1oAMvzu1y/abW0TL/2atvqptWAdIKpHNArZNZpDUjzBZ+mFJZ1aj0uX31+84juM4LUg5EwOTy5kjI8xsRBn27QgT04emr7Vp3AlwWgRJQ4H+ZnZSS7fFcRynEurr0vcExAd+qYf+B8B6ieN1Y1qO7oSgamPjirC1CZFi980yOdCdAMdxHMepgCoPB0wAekvakPDwPwQ4bEldZnOA1XPHksYSQr376gCnNMoTJlKQJ75B0sQo7rNPzDdU0rUJu4ckDS5QXiMRIEm7xtC+uTy7Sbo/7u+hIJg0RdLTBcprJGoU07+nIGg0WdIrcVas4zhOTWCWfmu6LFsMnEQIBPcacG+MH3OhglrtMsF7Alo5MYJfvjDRlYR1+wOBXsCzksoJt9dIBIgQNOl6SWuY2WzgaOAWSWsANwE7m9m7RSIhXk2eqBGwKXAGcKKZjY+REasyG9ZxHKcaVLknADN7BHgkL+28InkHV6NOdwJaP42EieJ40r1RlOhNSe8QFArT0kgECEBB/viIqKGwPXAkYc3rODN7N5m3QHmFRI3GA1dKuhMYbWazCtg6juO0CNV2AloCHw5ou+R3UBmwmIa/ic4Akn6Y6JbvX6LMWwmhiw8lOB5p173lRI1ymgfrmNm8GC3rZ0AXYLykRo6KpOPisMbEJ+a/lbI6x3Gc7FRzOKClcCeg9VNMmOhASe0k9QK+QxDxmQn0jenrEYYLMLP7Ew/oiRQWASIKFn1IGH64NdbzT2DnONmlmDBSQVEjSb3MbJqZXUqYNNPICUgKCO3uAkKO4zQj9XXtUm+1ig8HtHKKCBMB/Bt4CVgJOMHMFkgaD7wLzCBMTHm5SLGNRICA3ITCO4E1zOy1WP/suD52dFzn+imwW155xUSNTlNQWqwniBI9muFWOI7jVBUXEHKWC/KFiSSNBJ4ys3z1QAMOT1FeQRGgyE4EpyCZ/1HyHuBmNhIYGfeLiRqdnJ/mOI5TK9Qvx5E2c7gT4FQNSZOAr4D/a+m2OI7jLGvMnQBneSRfnriK5W6zLMp1HMepRVrD6gB3ApxWQ9b/jvPqF1Zs23GXAZnqfv/Bv2Wyr//ik0z2HdpVPrhZP+fTTHVnFQDSiqtksl9fXTLZ21dfVG4797+Z6iajgFAPy2af6XfXsVOmur9on1H8qArU8qz/tLgT4DiO4zgVUFfDs/7TsvxfQY0jaRVJP28iT09Jh5XKk8j3apFzF0oaUkH7UtVdRnl9Je2VON5X0lnVKt9xHKdWMFPqrVZxJ2DZswpQ0gkghODN9CA2s/PM7KkKTIvWnYsDUCZ9gSVOgJmNiUF/HMdxWhUeLMhJwyVArxht7/K4vSppmqSDE3kGxTynx7fz56LozsuSdmiqkigKdEDcnynpgmg7LRdpr4ggT37dQyWNkfQM8LSkwZIeStRzrYIMMJIGSHo+CgO9JGll4ELg4FjewUqIEsXrekZBdOjpqBOQa/uwWNY7ievoIWlcLOtVSYOq8o04juNUgXpT6q1WcSdg2XMW8LaZ9SVEz+sLbEWIl3+5pB4xz3MxIt9VxIA6ZtaPsH5+WAX1fhbthxOEeGCpIE9fYBDwdYG6AfoBB5jZ94oVLqkjMAo41cxy1/MVcB4wKpY3Ks/sGuA2M9uSEFQoeV09CDEG9iE4JhB6KB6P7d0KmFzuTXAcx1lWtIbhAJ8Y2LzsBNxtZnXAJ5L+DgwAvszL1wG4NobPrSNE5yuX0fFzEvCjuN9IkCch2pPkySJCP0m+C3xkZm0fJ6AAACAASURBVBMAzOxLgCLl5dg+0ZY7gMsS5x6IgkYzJK0V0yYQlAg7xPPuBDiOUzPUtYIlgt4TUJucDnxCePvtDzRaQyXp1thN/kj+uUhuvVsd0dlLI8gT+SqxX1BUaBmQXJ8nADMbB+wMfACMlHRkvlFSQOhxFxByHKcZaQ09Ae4ELHvmAt3j/nOE8fL2ktYgPOBeyssDsDLhLbse+AnQaEGsmR0du9z3yj9XjCKCPPl15/MeQea3k6RVgO/H9NeBHpIGxLK7x4mEpcp7Hjgk7h9OuB+l2rsB8ImZ3UTQK+iXnycpIPQDFxByHKcZaQ1zAnw4YBljZp9LGh+X9j0KTAWmEKR7f2lmH0v6HKiTNIUQT/964K/xzfcxGr6ZZ6GQIE99Xt0NopeY2fuS7gVeJYgLvRLTv4kTG6+R1IUwv2AI8CxwlqTJwO/z6j8ZuFXSmcBs4Ogm2jsYODOKFM0DGvUEOI7jtBQ1POk/Ne4ENANmlr8E78y884uAXfPybJnY/1XMNxPYvEgdQxP7PRP7EwkP01KCPPl1j8wr+5fALwvUOQHYrkB5+eHzRsb87xWoq1EYYzPrFj8bCB85juPUErX8hp8WdwIcx3EcpwJqeaw/Le4EOI7jOE4F1GVWLGl53AlwWg0PdV6Uyb4/a1Rs+8KZb2eqe/b8OZnsD93tsqYzlWDXditVbHvRLhdlqrtrRYEpl5JVAOiiiRdnsj9ym19UbDttwceZ6h7UpZIgoUu5ffZLmewn7Ta3Yts1lFFAqEO2/++HZ7IO1LeCSQG+OsBxHMdxKqAepd7SIGkPSa9LequQ5oqkX0iakYi6ukHWa3AnwGkRJM2Lnz0lfR1jHsyQdIOkdvHcxpIekfRmDIF8byKQkOM4TotiKPXWFJLaA9cBewJ9gEMl9cnL9grQP0ZdvY+GAdcqwp0ApxbIhVXekvDj319SZ+BhYLiZ9Y4hkK+HDH32juM4VaS+jC0FA4G3zOwdM/sGuAfYL5nBzJ41s/nx8J/AulmvwecEODWDmS2W9DywEUE34AUz+1vi/NiWapvjOE4+ad7wy2Ad4P3E8Sxg2xL5jyHEesmE9wQ4NYOkroSIhNMI8RAmtWyLHMdxirO4jC0Z4jxux1Var6QjCCHlL894Cd4T4NQEvWKEQQMeNLNHJe3W0o1yHMcpRTk9AWY2AhhRIssHwHqJ43VjWgMkDQHOBr5nZgvzz5eLOwFOLZCbE5BkOlBUyjhH9KaPA9hp1X5s0v07y6B5juM4jamyiOAEoLekDQkP/0MIw6JLkLQ1cCOwh5l9Wo1KfTjAqVXuAnaQtHcuQdLOkhqETU4KCLkD4DhOc1LNJYJmthg4CXgceA2418ymS7pQ0r4x2+VAN+AvcUXVmKzX4D0BTk1iZl9L2gf4o6Q/AosI4kuntmzLHMdxAtWOFWRmjwCP5KWdl9gfUuUq3QlwWoaESNBMiosi/QvYoxmb5TiOk5rF8rDBjuM4jtMmaQVRg90JcBzHcZxKSBkEqKZxJ8BpNeyzoEMm+5s6Vi7ic+4eCzLV3e+pjTLZj5rwh0z2E7ZqFKY8NcdPG5ap7qzYV19kss8iAARw+6QrK7ateydbKIz26xUcSUtvv/35meyveqly8ajFY4ZnqvuZsz/KZF8Nqrw6oEVwJ8BxHMdxKiCtMFAt40sEnWWOpLGS+ldgd76kM5ZFmxzHcbJiZWy1ivcEOI7jOE4FLF7+OwK8J6AtI+nIqEs9RdIdkkZGKd+Jkt6I6/SRNFTStQm7hyQNLlBee0lXSHo1lntygTyHSpoW81yaSN8jygVPkfR0AbtjJT0qqUvVboDjOE4GvCfAWW6RtBlwDrCDmX0maVXgSqAnQdKyF/CspHJmrB0X7ftGRcBV8+r8NnApsA3wX+AJSfsD44GbgJ3N7N0CdicBuwH7VyNWtuM4TjVoDRMDvSeg7bIr8Bcz+wzAzP4T0+81s3ozexN4B9ikjDKHADfG8JfJMnMMAMaa2eyY505gZ2A7YJyZvVvA7khgT+CAQg5AUpnr8flvldFUx3GcbNSXsdUq7gQ4+eT3XBlBCTP5W+kMIOmHMX715Eom/qVkGqF3Yd1CJ5PaAT/omm2ZneM4Tjm4E+AszzwDHChpNYBEF/yBktpJ6gV8B3gdmAn0jenrEYYLMLP7zaxv3CYCTwLHS1ohr8wcLwHfk7S6pPbAocDfgX8CO0f1rHy7V4DjgTFxOMFxHKcmMKXfahWfE9BGiepUFwN/l1RHeNgC/JvwsF4JOMHMFkgaD7wLzCCoW71cpNibgY2BqZIWEcb5l0woNLOPJJ0FPAsIeNjMHoQlksCjJbUDPiXMAcjZ/SMuFXxY0m65IQzHcZyWZHFLN6AKuBPQhjGz24DbcseSRgJPmdkJefkMODxFeYuBX8QtmT44sX83cHcB20eBR/PSzk/sP06Q2HQcx6kJannWf1rcCXAcx3GcCmgNqwPcCXCWYGZDW7oNWXimc7bpN8rwP3rhrGwdgz/Vepns696emMl+XMfKwy+M2+Y8TvvLfpnqz4LN/W8m+2kLPs5knyX+f/vvbJOt7tfGZ7I/aEG2/zNZfnf1H36Sqe43OrXPZL93JutALU/4S4s7AY7jZKIlHQDHaUlagxPgqwOczEhaQ9KLkl6RNKgC+yURCSXtL6lP9VvpOI5TXeqUfqtV3AlwqsH3gWlmtrWZPZexrP0BdwIcx6l5PE6AU5NI6inpX5LulPSapPskdZV0nqQJMW7/CEmK+QfEWP+TJV0u6dWY3j4eT4jnjy9QV1/gMmC/aN9F0vAYxW+6pAsSeWdKWj3u95c0Nq+sHYB9gctjWb0knSJpRqz/nmV20xzHccqkNWgHuBPQevkucL2ZbQp8CfwcuNbMBpjZ5kAXYJ+Y91bgeDPrC9QlyjgGmGNmAwghf4/NBfTJYWaTgfOAUTFo0NfA2WbWH9iSEBxoyzQNNrPngTHAmbGst4GzgK3NbEvghJIFOI7jNCP1WOqtVnEnoPXyvpnlpg7/GdgJ2CWO3U8jaAdsJmkVoLuZvRDz3pUoY3fgSEmTgReB1YDeKeo+SNLLhABEm5Gte38qcKekI2gdsTkcx2klVHs4IKqpvi7prRhYLf98J0mj4vkXJfXMeg3uBLReCmkAXE8Q4tmCEM2vcxNlCDg5ERp4QzN7QtLFOc2ARgahp+AM4Pvx7f3hRD1JDYKm6s6xN3Ad0A+YkAtJnKhviYDQq3PfTlmk4zhOdqo5HBBDqV9HEEzrAxxaYJL0McB/zWwj4CqCKmsm3Alovawvafu4fxjwj7j/maRuwAEAZvYFMFfStvH8IYkyHgf+V1IHAEkbS1rRzM7OOQYF6l0J+AqYI2ktwg86x0yCjDDAj4u0ey7QPdbXDljPzJ4FfgWsDHRLZk4KCG3evVexe+E4jlN1Fiv9loKBwFtm9o6ZfQPcA+Svv92PpVFe7wO+n5vbVSnuBLReXgdOlPQa8C1gOOHt/1XCw31CIu8xwE3xzX5FYE5Mv5mgF/BynCx4I03EljCzKYRhgH8RhhaS0UwuAK6WNJGGcw+S3AOcKekVwtDDn+PwxSvAsOi0OI7jtDhVnhOwDvB+4nhWTCuYJ4Zpn0MYpq0YDxbUellsZkfkpZ0Tt3ymx6574jjURAAzqwf+X9yKYmYjgZGJ46FF8j1HEBgqah/nMSS7wHYqVbfjOE5LUc50vyiSdlwiaYSZjahyk8rGnQAHYG9Jvyb8Ht4DhrZscxzHcWqfctb/xwd+qYf+B0Ayfvi6Ma1QnllxftTKwOdlNKMR7gS0QsxsJrB5GflHAaOWWYMcx3FaIVVe+jcB6B0nV39AmJ91WF6eMcBRwAuEeV3PRJXXinEnwGk17Log2xSX6R0WVWzbbc80KyeLc/nURgstyuKodc/MZL/Lor9UbNvu25tkqlvtM/4Zymg/qMtT2apfL7W/3YisAkDtN90xk/3YTo9lst9u3cpX/1rPqZnqXm/RvzPZV4NiE5sqwcwWSzqJMGerPXCLmU2XdCEw0czGAH8C7pD0FvAfGk7krgh3AhzHcRynAqodBMjMHgEeyUs7L7G/ADiwmnX66oAmkDSvQNrOkl6WtFjSAVnKyju/iqSfV9LOrCRFfCq075kLN1yFtows5746juO0BB42uO3yb8LkubuayFcuqxDC+zqO4zg1jgsItVHMbKaZTaWJ71bShpJekDRN0kWJ9G6Sno69CdMk5QJCXAL0Sgj5FMuXX083SbfGPFMl/TimFxPyGSDpeUlTJL0kqXs89W1Jj0l6U9Jlify7x+t4WdJfYrChUtfdOdGeVyTtEtN7SnoulvNyFAxCgWtjuMyngDUTZV2SEBC6olS9juM4zYmV8a9W8TkBy5argeFmdrukExPpC4AfmtmXCqp6/5Q0hiCWs3kuEl9cAtIoX4HZoOcShH62iHbfiulnm9l/YjjKpxWEfP5FWAlwsJlNkLQS8HXM3xfYGlgIvC7pmnjuHGCImX0l6VfAL4ALS1z3iYCZ2RaSNgGekLQx8Cmwm5ktkNQbuBvoD/yQIHjUB1iLEKDoFkmrxXObmJkp6Bw4juPUBLX8hp8WdwKWLTuyNDzuHSyN8yzgd5J2JvyO1iE8/PIplu/jvHxDSMwSNbP/xt2DYoCKFYAehIesAR+Z2YSY90uAGHnyaTObE49nABsQhij6AONjno6E5Sml2Am4Jpb/L0nvEYIEvQdcqyA/XMfSwEE7A3ebWR3woaRnYvocgsP0J0kPAQ81Ua/jOE6zUVfDb/hp8eGAKqLCwjqFfiWHA2sA28S3/k8oLKhTMJ+kE3P1SPp2kbaUEvIpxsLEfh3BeRDwZEJEqI+ZHSNp20Qb9m2i3Bynx2vYitAD0LFU5hgWcyAhRvY+QKP1TEoICD0+/62UzXAcx8mOSwk7DSggrDOepW/ohyeyrgx8amaL4nj5BjF9iXhOqXxmdl3iofwh8CShCx5YMhxQTMjndaCHpAExb3flKfPl8U9gR0kbxfwrStrYzF5MtGFMns1zueuNwwDrx3pXJvRC1AM/IayFBRgHHCypvaQeQG4OQTdg5bhs5nSC89CApIDQD7puVOIyHMdxqotPDGwbdJU0K7H9Ik6sm0VYr3mjpOlFbE8liPhMo6EQxJ1A/5h+JGGcHjP7nNDt/qqky4vlK8BFwLei3RRgl2JCPlGd6mDgmpj3SUr0EJjZbMJKiLslTSUMBTQVHeZ6oF1s9yhgqJktjOlHxXo3ITgpAPcDbxLmAtzO0uGG7sBDsd5/EOYiOI7j1AQ+MbANYGbFHKV1U9i+C2yfSDonpn+Wl560yQ8TWTBfns08QijJ/PShRfJPALbLSx5JQxGgfRL7zwADmmjDTGKo4hjQ4ugCed4Etkwk/SqmG3BSkaIHlqrXcRynpajlN/y0uBPgOI7jOBVQy2/4aXEnwHEcx3EqYHE27Z6awJ0Ap9VwT6f5mez7ZAhD8Pqw2Znqvr3D+pnsj9rpN5nsx85/o2LbPXe6oOlMJei6ZH5oZfSwbH/Gbp/9Uib79tufX7HtQQuydShnFQD69aTfZrI/cpvKp+msqaYWK5Xm/Y7Z/r9XIy758u8CuBPgOI7jOBVRy0v/0tLk6gBlENCRdIKkI8ttlKospBPD1R6WOO4vaVi1ys+KWpF4T6XfueM4zvJGW14dkBPQOaNUJjO7ocLyc0I61+efkLRCDCJTDj2Bw4iCP2Y2EZhYYducEmT4zh3HcZYrWsPqgIriBJQhoHO+pDPi/lhJlyoI1rwhaVBM3yymTY4iMb1pLKQzOArPjAFm5L/5SjpD0vlxfyNJTymI47wsqVcsb1As7/RY3kMx/6qSHoh1/zPG18+1/ZbY7ncknVLkGluteE9euYMl/V3Sg/F+XCLp8HgN0+J9zv/OT9FS8Z97Ytr3tDTS4Cu565d0pqQJMe8FMW1FSQ/He/WqpINLXbvjOE5zUkd96q1Wae45ASuY2UBJewG/IcS8PwG42szulNSREEUuX0hnMNAvpr0rqWeJOu4ELjGz+yV1Jjg6ZwFn5Na+x/JyXAC8Ymb7S9qVEKwmF/FvE0L0uu4EQZ3hZrYor75WKd5TpOytgE2B/wDvADfH7/NU4GTgtLz8ZwEbmtlCLRX/OQM40czGR4dmgaTdgd6EmAACxijoJawBfGhme8d7u3KJ63Ycx2lWavfRnp7mdgJGx89JhC56CNHhzpa0LjDazN5UEKrJ56UYfKco8a1yHTO7H5YEraFIeTl2Ior8mNkzklaLD2eAh2Oku4WSPiU8JGfl2bdW8Z5CTDCzj2Ib3waeiOnTiKF+85gK3CnpAeCBmDYeuFLSnYTve1Z0AnYnRDgE6EZwCp4D/iDpUuAhM3uuiWt3HMdpNhoLui5/VDVssAoL6CTJCdTkxGkws7uAfQlvvY/Et/FCfJXYX0zDtmdba1KcRoI6aiPiPUXqSLaxPnFcT2GHcm/gOkIvzgSF+RyXAD8DuhCcm03idf4+cZ0bmdmfzOyNaDsNuEjSeQXauURA6K15M9PcB8dxnKrgAkJ5FBDQaRJJ3wHeMbNhwIOEsLL5Qjr5fAKsGd/aOxEU5jCzucAsSfvHsjtJ6tpEeUmxm8HAZ7k39CLX2CbEe5qoo0kktQPWM7NnCeGBVwa6SeplZtPM7FJgAmHI5XHgp3F4AEnrSFozOlnzzezPwOUEh6ABSQGhjbr1LLeZjuM4FdMaBITSDAd0VRDLyXEl4UFzP/At4H8kXWBmm1XYhoOAn0haBHwM/C6OpY9XmPz3KOFNeglRVe9C4CXgAxoK6/yEIOpzIbCIIPIzFahTEK4ZydJuZ4DzgVsURGrmUyAGfxNcBFwX21oHXGBmoyXlxHveJyHeEye3XSOpC6H3Y0ixgs1stqShBPGeTjH5HKBUZJfrgeEK4j2LieI9kq4H/qqwfO8xGor37EqYC/Bvmh5uSEt74M9xHF/AMDP7QtJvFSYr1gPTgUdj+zYFXojDHvOAI4CNgMsl1RO+y/+tUtscx3EyU8tL/9Ki1jCm4TgAh26wf6Yf8xpL/KzyObp+QZaqWbg4W9S8qztkjDw3J0PEwFX6ZKq7pSMG/m72+Ez2R6+5bcW22SMGlhzVa5LlOmKgZYsYOPq9MSUni6Vhz/X2TP0359H3H624PkmrEiaV9wRmAgcl5p/l8vQFhhN6ouuAi81sVFNlu5Sw4ziO41RAMw4HnEWYPN4beDoe5zMfODL2yu8B/DGxKqso7gQ4juM4TgU0Y8TA/YDb4v5twP6N2mL2RpRrJ85X+5SwzLokrh3gtBp+/M2Kmezv6DCnYtt1es/NVPfBr2fr1n1i0o2Z7KdtfXrFtltMvKzpTKWor8tm/sUnmewn7Zbtu7vqpYsqtq17O1vg0u3WzTYUk6U7H+D2SVdWbLvovqsz1f2P32T73qtBM876Xyu3PJswd26tUpklDSSsAHu7qYLdCXAcx3GcCihnTl2MG3NcImmEmY1InH8KWLuA6dl5dZqkohXHVV53AEfF1WAlcSegBZA0z8y65aWdD8wzsyvy0tcG/ggMAL4gLI88La6hb9K+Wu0r0/4R4DAz+yJrWxzHcWqVcnoC4gN/RInzRVeKSfpEUg8z+yg+5D8tkm8lwmq6s83sn2na5XMCahiF9XL3A2PNrJeZbQP8mia6gloaM9vLHQDHcVo7dVafesvIGJYuXz+KEFOnAQph9+8Hbjez+9IW7E5AbbMLsCipzGdmU5oKnyupr4IY0lRJ98cARkg6VkGkZ4qkv8ZASkjaUEGkaJqkogOcCjLDw2PZ7yiICt0i6TVJIxP5ZkpaXUUEgBTEh3LCQlfEtDVimybEbceYXlBwyHEcp6WxMraMXALsJulNQmyZSwAk9Zd0c8xzECEM/NDE38wmA/f5cEBtszlBZ6FcbgdONrO/x6BJvyGI+4w2s5sA4sP+GILOwNXAcDO7XdKJxQqNfAvYnhDqeQywIyEM8ARJfc0sGTJ6D/IEgCStRhAt2iSObeWWsFwNXGVm/5C0PiGK4KYUEByq4H44juNUneaaGGhmnwPfL5A+kfD3lxhZ9c/llu09Aa2MGKFvFTP7e0y6jeAdAmyuICc8jRBaOBflcUeCqiCECSWl+JuF2TDTgE9iCOBc9L+eeXmnEbzXSyUNigJJcwgP8j9J+hFhbSsE7/ZaBd2JMcBK8aGfExw6JV7X4vR3w3EcZ9nh2gHOsmY6sE2hE2parKkQI4GTouzxBTQUNWr0Ky1SR1I0KF9QqEHPUiEBoPgQHwjcR9B8eCxmbwdsl9ArWMfM5hURHEq2cYmA0JPz30p/JxzHcTJiZqm3WsWdgNrmGaBTXFoCgKQt41t1QbGm+Lb9X0mDYtJPgFyvQHfgI0kdiCJDkfEslUNekl6JIFQSFRAAim/3K5vZIwRlw61i9ieAkxO2feNnIcGh5PUuERDaretGlTTTcRynIuqoT73VKj4noGUoJMoEcI6k03KJZraupB8Swj/+itCNPpMwvl+Ko4Ab4sS/d4CjY/q5wIvA7PiZm2R3KnBXrKPRrNMMbEFjAaDuwIOSOhOEhXLRSk4hCDFNJfwuxwEnAKflCw5VsX2O4zgVU8tv+GlxJ6AFMLNiPTDnF8j7IWHWZ1Nlnp/YnwxsVyDPcILARH76u4TJfjnOKVLH0MT+TMLExULnesbdx+OWz8ACZX8GHFwg/eT8NMdxnFqglsf60+JOgOM4juNUgPcEOE4NMbVyJWAA5iyufPXhQa934P7tK1+48NJ/3qzYFmDR6Gsy2ffY4MuKbT/b/xhWPrKiaSMAqF1GRdeO2b74LBLSAIvHNOpcS039h9ni31vPqZnss8r5Zon/3+GAUzPV/e5F52WyrwbeE+A4DkAmB2B5J4sD4DjLM1VQB2xx3AlwkFRHWMaX4564NC9LmYOBb8zs+Xh8AmGlwO1ZynUcx6kVqhAOuMVxJ8AB+LrSZYAlGAzMA54HSIY+dhzHaQ3Ut4I5AR4nwClIDPH7uqTvxuO7JR0b98+M8f2nSrqggG1PwvK+02OwoUGSzpd0Rjw/VtJVMcjPa5IGSBot6c2kdoGkIyS9FMu4UVL75rh2x3GcNFgZ/2oVdwIcgC4JwYnJkg6OQYdOAkZKOgT4lpndJGl3oDdhmV9fYBtJOycLi8sHbyBoAfQtInj0jZn1j/keBE4kLDkcKmk1SZsSlgzuGHsp6mgY4MhxHKdFqTdLvdUqPhzgQJHhADN7UtKBwHUsjey3e9xeicfdCE7BuDLrHBM/pwHTzewjAEnvAOsBOxFCJk+QBCFscEENbcdxnJaglt/w0+JOgFMUSe0ISn7zCeqBswhR/n5vZjfm5T0RODYe7pWi+KY0CATcZma/bqKNxwHHAey96kD6dffQwY7jNA+1/IafFh8OcEpxOvAacBhwa9QceBz4adQAQNI6ktY0s+sS4j8fAnNZGpa4Ep4GDpC0ZqxnVUkb5GdKage4A+A4TnNSb3Wpt1rFewIciHMCEsePAbcS1PsGmtlcSeOAc8zsN3G8/oXYTT8POILGXfV/A+6TtB8JYaC0mNkMSecAT8QeiUWEeQPvlVuW4zjOssCDBTmtAjMrNut+00SeXyT2rwZKhgqLMsJbJpKeS5wbnNgfC4wtcm4UMKp06x3HcVoGDxvsOI7jOG0U7wlwHMdxnDZKa+gJUGu4CMcBeGytQzL9mJ/tUrmQzddkCx/6bv1XmexPXtgtk33vtT+v2PbNj1fLVPcisgkIfdE+WwypMR3mZbI/ckGXim3f6JSt7estyvb3+66OczPZn7Cga8W273bI9g7608kXZrLvsPp3MipXwdqrbJr6C/j4i9cy17cs8J4Ax3Ecx6mA1vAS7UsEHQAknS1pegwFPFnStsuwrrGS+i+r8h3HcZqDeiz1loW4RPrJGFr9SUnfKpF3JUmzJF2bpmx3AhwkbQ/sA/Qzsy2BIcD7Ldsqx3Gc2sbMUm8ZOQt42sx6E2KonFUi728pI4KrOwEOQA/gMzNbCGBmn5nZh5JmSrpM0rQo5LMRgKQ1JP01ighNkLRjTF9R0i0x7ysxRgCSuki6J4oF3U8IAUw8Ny+xf4CkkXF/pKQbosjQG5L2aba74TiOk4Jm1A7YD7gt7t8G7F8ok6RtgLWAJ9IW7E6AA+EHs1582F4v6XuJc3PMbAvgWuCPMe1qgjjQAODHwM0x/WzgGTMbCOwCXC5pReB/gflmtinwG4ImQBp6EoSK9gZukNS54it0HMepMs3YE7BWTl8F+JjwoG9ADKr2B+CMcgr2iYEOZjYvepCDCA/vUZJy3U13Jz6vivtDgD4xYiDASjGM8O7AvjnJYKAzsD6wMzAs1jVV0tSUTbvXzOqBN6Ow0CbA5CZsHMdxmoU6S78qKKlzEhlhZiMS558C1i5genbywMxMUiGv4ufAI2Y2K/G3uUncCXAAMLM6QuS+sZKmAUflTiWzxc92wHZmtiBZhsIv78dm9npeesmqE/v5b/r5P/RGP/zkf6yTu/dnry69StXlOI5TNcrp5o8P/BElzg8pdk7SJ5J6mNlHknpQWFF1e2CQpJ8T1F07SppnZqXmD/hwgAOSviupdyKpL0tj9B+c+Hwh7j9BQg9AUk6G+HHg5OgMIGnrmD6OIEKEpM1pGE74E0mbxq6sH+Y17UBJ7ST1Ar4DvJ53voGAkDsAjuM0J1bGv4yMYemL2VHAg43aYna4ma1vZj0JQwK3N+UAgDsBTqAbcJukGbGrvg9wfjz3rZh2KkFVEOAUoH9cTjgDOCGm/xboAEyVND0eAwwHukl6DbgQmJSo+yzgIeB54CMa8m/gJeBR4IT8ngfHcZyWpBknBl4C7CbpTcJw7CUAkvpLurmkZRP4cICDmU0CdshPjy/0l5vZr/Lyf8bSHoJk+tfA8UXSDylS933AfUWa9pSZnVDknOM4TovSXMGCzOxz4PsF0icS1F7z00cCI9OU7U6A4ziOs/TEZAAAIABJREFU41RAfRkTA2sVdwKcosSxpZaqe2hL1e04jpOG1hA22J0Ax3Ecx6mA5d8FoLxgB775tjxvwHFt1X55brtfu197S9TdVjZfHeC0JY5rOkurtV+e257Vfnlue1b75bntWe2z1t0mcCfAcRzHcdoo7gQ4juM4ThvFnQCnLVE0ZGcbsF+e257Vfnlue1b75bntWe2z1t0mUJxA4TiO4zhOG8N7AhzHcRynjeJOgOMsB0jqLGmllm6H4zitC3cCnFaLpB0lrRj3j5B0paQNWrpd5SLpZ8ADwF8l/a6Z695O0mOSxkraP0M57ZYHJ0bSGpL6FEjvI2mNlGV0KpC2aoY2LRf3rlaQ1FHSlpK2kNSxpdtT67gT4LRmhgPzJW0F/B/wNnB7OQVIWkvSnyQ9Go/7SDqmDPuyHRFJ++YlDTGzPcxsN2DvMurulXsgSRos6RRJqzRhs3Ze0i8IEs97sVQVMm39d0laKV7/q8AMSWemtF0xyksjaWNJ+0rqUEbdlTqA1wCrF0hfDbg6ZfWjk22N+u9PprTN2WS5d5dF2w6SnpY0W9IRZdR9oKTucf8cSaMl9SvDPut3p/idnReP15c0MKXt3oT/58OAa4G3JO2Ztu62iDsBTmtmsYWZr/sB15rZdUD3MssYCTwOfDsevwGcVoZ9JY7IFpIelNQ3Hk+VdLOkm4DpZdT9V6BO0kaEmdLrAXc1YXODpPMkdY7HXwAHEByBL8uoG6CPmX0J7E+Qg94Q+ElK23FAZ0nrAE9Eu5Fl1F2pA7iRmY3LTzSz54AtU9b9AHCvpPaSehJ+P79OaZsjy73bPdruA8wENgJSORCRc81srqSdCLK1fyLcz7Rk/e6uB7YHDo3Hc4HrUtr+AdjFzAab2feAXYCryqi7zeFOgNOamSvp18ARwMPx7ST1G0lkdTO7F6gHMLPFQF0Z9mU7ImZ2MUGS+efxwX8LcClwjZkdVkbd9bG9P4y2ZwI9mqh7f+AV4CFJRxIcnk6EN+FyhwM6xDfA/YExZraI9OHWZWbzgR8B15vZgcBmZdRdqQNYKk+q346Z3QQ8RXAG/gacYGZPpLFN1lXg3qUlpwmzN/AXM5tTZt253/fewAgzexgop1s963e3rZmdCCwAMLP/llH/XDN7K3H8DsGJcIrgToDTmjkYWAgcY2YfA+sCl5dZxleSViM+vCRtB5TzR7VSR+QrwgP4WsJb/KGEXohyWCTpUOAo4KGY1mTdZvY34AfAysD9wBtmNszMZpdZ/42EN9EVgXGxOz5tb4IkbQ8cDjwc09qXUXel9/0tSXsVaMyehAdKqQb/IrcBnYH1gcnAdjGtHArdu7S/u4f+f3vnHm/tWOf/98cjIR4041BCQiQhh3Kaic4HhxgyaBwqKr0mh2ZKUyI0wlBGKZoJIeFnTI45JaEMz+MsaoQ8U1IpepAc+vz+uK717HvvZ+21rnvd6/A8e3/fr9d+7X3fa32v+1r3Wntd3+u6vt/PV9J9wMbANTmW4Zka1/6lpFNI/z+X5S2lOmNF0/fuOUkzGPufW57shBcwS9JlkvaWtBfJCbtF0k6SdqrRh2lD6AQEQQckbUzaX1yPtDe7PLCL7TsK7VcCdgdusX29pFWBrW1PujQt6SjgDaQZ3UW2v5zjBA4ETu9kO6GddYGPAD+2fY6k1YH32T6mg832wEHA88C/klYFDgVWBj5j++cl1+7Q/qJ5daLb895EWsa/0fYxkl4FHGj744XXqX3fs92rSQ7Tj4DZ+fQmpOXpbW1P6ohJOqxT27Y/X9L33Nbqth+sHIu0VfG/hfYvBZ6w/UKOK1g6O8IltksC7wTusv2/OabhdaWrGX147/YgOSAbAWeQtqM+a/v8AtvTOjxs2x8o6cN0IpyAYMohaS4dlp1t14q0lrQosDYg4Kc1l2ZrI+l22xvmL/7Ztjeq9ONjtksD1JC0BLCq7Z8WPv9OkgOyBHCF7Tfk82sBR9r++xrXXpHkSLzc9ruyU7K57f+s0caSeWl5aOSZ7+4kxw9SHMa3bdeZTTftw62t971ybrbtjTvYdJzp2v6vGtffCljL9ml5Jr5U1SkpbKPn907SOsBbSP9z19i+t5d2gu4s2v0pQbBwYbsV2Xwk8AhwJunLZA+67IlPRNLPgeNsf71y7hLb2xba70Taz18h90Gpix0dkbslnUoaiK+rvK7nKY9QR9J2wL+R9lNXz4GGR9iemH1Q5QnSXu6SwG8q1/5foNgByJwOnAZ8Jh//DDiXFGjWre+b5+ctBayaA/w+bHv/kgv3eN9bvIsUA3Gl7StKrjfh2leRVosez8fLAd+x/Y4C23VI++fLTBjUZ5K2GDqxXf69ArAF8P18vA1pZaPICcgrGpuQHN/TSNsoZwFbFto3eu8yjwLXk8aoJSRtZPvWgmufRpsJQKwATE44AcFUZnvbG1SOvybpDuBzNdp4DthG0htJX2TPkpbGSzkW2K7OTMb2+yW9DnjO9n01rjWRw0mz+h/kdm/PS7Od2JEUf/AcaTbchL+2fV7em8f285JKgyq/TIpLuCjb3iHpb2tcu/Z9B5B0MmkQ/hFwpKQ32K6VGgks33IAIAW2SVqh0HZtUlT/sowN6pCC2/btZGh7HwBJV5KyCx7Jxy+jXnT+jsDrgVtzu79SThkspNF7l533vUkZHa0B3cCbC8wvqfy9OOm1/Kr02tORcAKCqcxTeX/xO6Qvkd1IAXd1eNr2rpI+CVwvaRfKI9wBHu1lKdP2XXVt2vCc7SfSrsI8OgZY2f4dKVe+HzQKqrQ9Z0Lf62Rl9HTfgb8FNsh76UuSZqN1nYAXJK1q+2GAHNRX9Jmx/V3gu5I2t/3jmtdtsUrLAcg8SgpSLOVZ25bUet9eUrcDDd+79wFrZIe77nUvqB5LOge4oW4704lwAoKpzO6k5fMTSV/CN1J/disA28dKupWU91xH/W2WpHNJ6WJ/bp2ssz/bgHsk7Q7MyHv6HyfNcIfFwaTZ4BqSbiQFVe5caDtH0haAlVLlDgDqDOq93vdnbb+Qn/u0JoxkhXwGuEHSdaTPz98A+5UYSjqJMadpt4mPFwbXXSPpCuCcfLwrKWWxlPNydsCykvYFPgB8o4Z90/fubtJKyG+6PbGAtUjbI8EkRGBgEHRA0nY5Za51vBqwl+0jCu3bRSsPJUo5z2Q/A7ydNBhdQQruG2aAW09BlZL+muS8vTXbXgkcYPuxQvue7rukp4FWnrmANfJxK6agSDAo93+zfHhTXmEpsdur0+O2zyhsZyeS8wHwQ9sXlthV7N9G5XNju1jxsA/v3SbAd0nOQNWB6xTL0rJtBQUr//418OmJKwTBGOEEBFMWJdW7D5L2eOcFVZUMwJLWsX2fJpFLLQlS6gdKqmurUVm1cxtFu4J2ZgAvcVKSGwqSPgacPSFAbjfbJw+rD3VRF2lh278obGd70tYCwA9sX9Lp+QsSefn/mbwlsjbJibt80FkxlevfQ9JJuIvK9pXt6yY1CnomnIBgyiLpfOA+0hbAEaTsgHttH1Bge6rt/SRdm0+N+0exXRKkhKRXkPbYW5HV15NmRf9XYHsMaSn3J4ztqbpkRpTtv03SCXgBuIUUYX6i7a6CSQ2j61tt3G57wwnnbrP9+g4285bD21Ej17zn+94USV8ENgXOzqd2I+kV/EuNNq6lfZR7189djr04CXgNKTNkBvBU6XsnaTZpFWE50n76LNI2yR6F9scCRwF/Ar5Hkls+yPZZhfa32N605LkVm461DYbltC+MhBMQTFlaA46kO22vn/cnr7e9WYHtG4CHnQVW8jLt35FU3A63/fvCPlxF0us/M596P7CHUzGgbrY/Bda3/eduz53EvqU3sAdJeOUQku5A1yVtSffTQ3T9hDbuIvW/tcc9A7jT9qQSsn1cDu/pvmtyjYliJ0hJa2FD23/JxzOA20q3ErJNVQ9gcdJn73nbnyywnUVK5zyflOq3J/Bq20X1C5Q1CiT9I7BEjoeZz6HrYN/63O1IynQ4mLQlsUEX05b9CaRtgIsYvx0w6UBecdYXJ73mO0jv2frALNubl1x7OhKBgcFUprV8+bik9Uj7g6VBQl8n7WmS05uOBv4R2JAk41sa4La87er+9OmSSgsQPUDK0e7JCWC8/vxXbD/XivguoNfo+irfA87NQWaQ6iF8r5NB6SBfQE/33Vljog8sC7QcxWXqGtuePeHUjZJurmF/v6QZOcjxNEm3UV7ESBqT/W1VzKwj+ztf7YKa8ZWtlaKqs94xRdD2NgCS/gvYqJVdk//vD69z8elGOAHBVObUvA99KGlWsRTlGgEzKrP9XUmFVC4ALpB0e40+PKZUxrUVqb0bUBQgBTwN3C7pGsbPiIqWxBnTn7+D+tr9/chq+BRp4P9oPr4K+I8SQzUQ3Mk0ue9NORq4Lc9ORYoNOKROA0qyvy0WIdUBKHUmnpa0GOmzcyxJMKuO9v+BJIfhQtv3KGlLXNvFpkqrdsGfgI+qZu2C1oDeI2tX02tt3y3pNQ3am/LEdkAQtEHS3aQl3efzF9p+rYA8SXfbXq9zC/PaWY20P7s5aTbzI+DjrRzyLrZtl8abzJZVrt0/sqyGfP3a8QQTntvzfe8HSgI9rX3tm12o21+xf5CxKPfngQdJao9dc97za3+UFA9wEMl5ONnjq+uV9mMRkmRwrYBSja9dsCQws9s9kPR+22dpkmJLtk8ouO45JC2QVvzBHrn/86VbBolYCQimLGqmXX8OcJ2k35FmNNfnNteknuDNL4CiQL42to2WxiUdQJJ9nUuagb+eNCPtWgjGWX2u4fW3JC3FtrIbWvvq3VQLoYHgDjS7773SJjitFYT4ckkvrxOcZnv1Xvth+xd5JWBV4AJq1rtoF1AqqSigNNvvAnwvOwCfJcWjHEXajutES5So3ZZM6Xu/D2nlqRX8+0Pga4W205JYCQimLJIuJ2vX295AKWf9NtuvK7TfjFRr4ErbT+VzrybNLDp+oUv6ZA6oahvt3mlJX9J5tt+XA+va2Zbmqt+RX/c7SMvyhwJnekJhmn71u01b95FmorOpKMa5IF9c0jtJsRfjBHfcRcu/n/2vSyU4rR0uzSiptLcesC7j01u7VpCUtDWp+t5DpHu3Cknboii1tElAabZvBeJuRRr8jwM+Z/uNhfZb2r6x27kO9rWKZk13YiUgmMo00a7H9k1tzk1aSnYCraC6WaXXq9CaxRQVKepAKxrr3aTB/x51j9Bq0u+JPGH78l4MbX8vz6xbwWEHukxwp5/9r0XDvexxKBXx2ZrkBFxGKmp0A1BSRvp44O2tQTA7rueQ4gpKaBJQCmMO33tIsTSXKpXHLuUkkvPR7dx8KOkzHEe9olnTmnACgqlMI+36JnhMZfBpT6iDnpdLO9k+kn8XCdN0YLZSMZnVgU8rFYHpVjvg4vy7H1H610o6jlS9rijVq4WkC0iV6C5rpdqV0OS+9wv1RyRpZ2AD0srVPnlrqyjPHnhRdRZs+2d5UC+lSUApwC9zRsjbgGOUSjN3DUzMGQlbAMtPiAuYSXl2wmHMXzSr562V6UBsBwRTljyTPIlUF/5usna97TuH2Id2deHnOzegay9CSml8wPbj2SFaeVivf5Ll8aJlcUlvJe3vbkbKdz+tzvLuiO97o6DG/Pybbb9BSbhnG1Jcx7221ymw/SbJ2asGx81oEtRZGlCan7sk8E7gLtv/m4MkX2e7YyyKpDeRVj8+QkrRbTEXuNipnHW3a99ke7Pq/W5tT5T0fToSKwHBlERJoOVN+ae2dn0frv8u0jL8ypL+vfLQTFK09yCvvY5TCeLWQPSq7rsA/afJ8rjtq4GrJS1DSu+7WtIcUiGbsyZ7H0d53yvMkCR7nEjSYjXbmCVpWdLrnQ08CZRWFfwo8DFSwShIQa1dVyG6RecDHaPzJc3MWQSLk2fiOUvgzxRszzjJAl8n6fTWKpiklWpmVoy6aNZCR6wEBFOW1mxqRNfegDQIH8F4bYK5wLW2/1CzveVIJWK7zuI1v+RxldoBar3SMDuDvHLxfuAfSDXhzwa2Is0qt57Epq/3vRfyFshqpGV1SEGZc2x/osB2S9s3Snqxs1KkpFeSUuwGuoIj6cO2T8nxCPNh+/Nd7C+xva3GpzdWzIuyQia2WWv1RuOLZkEqmnWUh1g0a2EjnIBgyiLpSyTFvXNJucPAcHXEJc0k6ba/kI9nAC+2/XSB7Q9IaW6LkmaDvwFutD3ZTK1v5GCyrwEr2l5P0vrA9raLA7yaZGdIupC0gnMmcHorTiI/Nsv2Jl3se77vTcnbMPuRFSfJIkmtvnSxnW174162LjRJNkmLhXFJvO42SsVuyWG811OBcAKCKcuoZ8K5DzcBb7X9ZD5eipRyuEWBbav2wYdIqwCH1dnfzAPfe4BXMr4KYYnoynXAPwOnVPZWi0WS8vNvsb3phP3ZIg16SdvYrqNSN9G+5/veTyRtVMfpzP2+kxSZ/52Jj7tzamm/KiCuTpLIfiXjPzfFEfbZaZxoX0dtstXO/nUCKiVtQdLEWMr2qnll6MO296977elCxAQEU5Z+pmw1YPHWQARg+8m8ZFnCojmo6n2kJc66XEySax1XkrWQJW3fPCGWoO6eepPsjHWz89BrhH2T+95P/oOC1LYK25JWEN5BWv0ppt0gL2lb1y9j/N+kzIyLqf+5aQUmrg/cU7E3KUuktI2tgLVsn6wkO7yU7QcLTL9EuncXAdi+Q6n2RzAJ4QQEU5acmvR3zD8jOWKI3XiqOhtUqg73p0LbI0h7mjfavkVJw71rhHSFVzRYAv6dpDUYG8B3JmnQ1+Fg0pfxGpJuJGdnFNrua/urrQPbf5C0LwUBbpkm972f1IrIdNJC+I6ke23f0YfrHwHUdQKesf3v3Z82KZvZXrdX4xyTsAlpO+g00pbeWYyVhe6I7TkTnNdibZDpSDgBwVTmu6SZ52x6r8TXlAOB8yX9ijQgrEQqSNQVpzz38yvHD5CcmlIul/T2bqlZk/AxkmLfOpJ+SdKuL6onD33JzmgaYd/zfe8zHYPpJqPqAPQSH1Chl7SQE/NAfCU19R0yP5a0ru2f9HBtgB1JEte35uv+SknjooQ5eUvAStoIBzAmIBW0IZyAYCrzCtvvHGUH8gx+HdJACDUGwj4E590EXJgD1Z6Dedr9M8u67rdKegmwiO25qiG64qQbv5vtL5GWhetSuwzxhOv3fN+bMmH5+fetYxfK9rZrskF3PtyDzetIGRlvZvxyfmkszbdIjsCvSU5E63NXuir1rG0rqxTmz2ApHwFOBFYmZZRcQXJog0mIwMBgyiLpVOAkV0qLjqAPe7Y77zIN+EbBeTlVaweSaEutf/R2s89W5HqNNnrOzmgSYZ/te77vTZF0ceVwcZKC3exeA1IlHWX7szWevyTwCZJ+/r45X37t0tgASfcD69p+tsf+3k/aChoXi1IjMPGfgLVIioNHAx8Azmm4RRFMQqwEBFMOpTLAfyF9vveR9AC9zUj6waaVvxcH3kJa5iwZjJoG580B7q7jAOTZ82uBZSTtVHloJpVCNoW0sgCqMRhFM0onqeCvA1+vG2GfaXLfG2F7u+qxpFWAL9dtJ0f7r2X7s0pFcRa1PbfA9DTSFtjm+fiXpG2l0tiAu4FlSSmpvfBb2xf1aIvtf5P0NpJU8dqk4kNXldjmuJkTSUqTJgksHZS30oI2hBMQTEVWZmwAGim2/7F6rKQCN1/q1yQ0Dc57APhBztev7u12ShFcmxShvixQHczmAvvWuHY/szPqRtg3ve/95v+A19QxyEGQ+wEvBdYAXkFyit5SYL6G7V0l7QZg+2mplmTkssB9km5h/OemNEXwNqVyxBdPsC/KDpD0QSdBqavy8QxJh7mLWFHm28BXSXEFAH9PKp5UVMFwOhJOQDAVebB06XEEPEUq6FNCu+C899e41oP5ZzEKg+psfxf4rqTNbZfK1LZFDRUDq0016Uemzn1vhMaXMW7Vb6i7kvEx0jbC/wA4afCvUGj7bF45aDmPa1AvMLatYmANlsjXe3vlXJ0UwbdI+jvggyQn6HRSSekSlrR9ZuX4LEn/XGg7LQknIJiKrKDJ9c+LxHL6Rd4frg4I6wLnldjmJcxxwXl1rl2dOam+BvttStXwXsv4evZ1itCcTlYMzMc/I8UH1HUCakfYN7nvfaCqk/88aT/7xppt/Nn2s60JvJLaYum2zmGkIMpVJJ1NSq3bu/TCThr+revW1hmwvU+d57ex313SrqSYgqeA3Wvcv8slHUJa9TEpI+QypRoG2P59k75NRSIwMJhySHqEFFXfdgZZuKzYr768qXL4PPAL2/9XaHsAaRCdSyoksxFwSC8pf3XTzCSdD9wH7E7a09+DVMXugBptNFEMbCvwUhph3+S+9wNJiwHrkAain9YNspN0LPA4sCdJvW9/4Ce2i0SjlESaNiP9D9yU9Qdq0zA9sSf7HMh4BskJeA3wE+Bgl0lttwSFWgNb4/oFU51YCQimIo8MWRCoLTm3/fAGe+MfsH2ipHcAf0VK2zqTlL9duzs1n7+m7V0k7WD7jLzHe33NNpooBlaXcOdF2FMQVNiH+94ISe8mFQ/6Oem+r65UnOfyGs0cQloOv4uU5ncZKTailMWBP5C+49eV1GuKYtOtmF7sLwY+ZvuaHMtwMHALaVWq/UWkTUlFmlbPx3uRNDUeIn0WYgVgEsIJCKYiw6+b24acK/8XScvYLh38qrRex7uBb9m+p2aAV5Vv1Hx+K6f+cUnrAb8GSvekW/SsGNgkwr4P970pJwDb2L4f5u3JXwoUOwE5O+Ib1H/fkHQMaRl8omxvL05ALzoDVS7tweYNTiWJyZktx09Iu2zHKeR00ryKdDRpBWVDUlxNqVLltCOcgGAqUhJBPSyeBO6SdBXjc+UnLQRTYbakK0kBbZ9WUk2rpeWu3jXYT1XS6/8saSBfCji08Jq7OKkd/oHeFQMnUjfCvsl9b8rclgOQeYC0pVOM2lcEfIIUb3CU7cc6mL+XpAtQSyVzQkpo9fwroLcCQDX1DT5p+1jbf6x8hlrsDfxLB/MZldn+rsCpti8ALpB0e91+TyciJiAIBkhelpyIC8WCWpHlD9h+PAc3vcKFdeVV0WC3/WpJLwfOt91Rgz1fd2fbPQXStfaBm+wnTxJh/5DtouyIJve9KZK+BqxGCkQ0sAvwMHB17kTXwTTHBLxASnmDlOq2JGlFZquJKyUTbC8HdnGlgFJhv0/Lf64AbAF8Px9vA/zI9rZd7OfSuZRxR6XK6udl4men22cpa4NsaPt5SfcB+7W2P1Sz+uV0I1YCgmCwLGv7xOqJHPBXwubA7bafkvR+UmDgiV1sqvSkwW77L5I+Se/R9I+1VjAkzScaU5hv3jTCvsl9b8riwKOkVRCA3+Zz21GeKvfWCYPeXRXnqq0jVHGcngZul3QN4/P0O66CtKL683u3ru1H8vHLSJkeHbG9dH7+kSQ9izNJK0B7AC/rZs/4bbyJ217dtsHOAa6T9DtSoajrc1/WpDwOZVoSTkAQDJa9mH/g3rvNuXZ8DdhAqSb6J0iBYd9ibHDpRhMN9quV5FsnSv6WBFi9h+SwnAkcX+Oa88jBiOMi7Gs20eS+N2UR4ACPL4N8fM3UuRmS3mD75tzGpsCM/NhkqpEtx2k2uZRuj6zScgAyjwKr1rDf3vYGleOvSboD+FwXO0/yd7vj8Q/aX8hOz8uAKz22xL0IKTYgmIRwAoJgACipte3O/LPhmUBppPLzeRDfAfiK7f+U9MEa3ThPqQDPskoKdB+gPNCsVXGvWnzFQNcUq5wOd5OkLWz/VtKSJeldVXqNsO/TfW/K+i0HAOaVQX59zTY+BHxT0lKk1/9H4EPZkTu6nYHtM2Ces/eMc52FnC3x4hrXvkbSFaTZNaTPwtU17J+StAdjufq7UXEkO7CBpD+SXu8S+W/ycVfJats3tTn3s+JeT1MiJiAIBoCS7vvqpC/sQyoPzQXutN21BoBSAaHvAfsAf0vScr/D9utq9ONtJOU2AVe4UIO9H0janCQMtJTtVfOKxodt719gex+w7cQIe9vrdLFrfN+bkme9W9v+Qz5+KXBdnfet0tYyAHWyHCTdRNpOeDIfL0WaHW9Ro42dgL/Jhz+0fWEN21eSVly2JDkBNwIH2n6otI1geIQTEAQDJM/K/pT32V9NWt6+vCRKXtJKpFntLbavl7QqaXAZeHDbhH6canu/Huz+h5SadZFrVkFUFhqqHAu4uXqui33P970pShUM/4VUtAdSYOAXPF7OtqSd9zC/YmNX/Qu1EWRqdy4IILYDgmDQ/BD4m7wvfCVJ9GRXUrBUR5xkfk+oHD9MjSp4eTZ3DCnaW/nH3aK027BJzefPw/YcjZc2KCoFDMySdBnjI+xvaaWxFUTY93zfm2L7W5JmMSZstJPtn9RpQ9LXSdkA25BiQXYGbi40f0qVyouSNiYFy5VeezPgJFJK5mKkWISnSj83khYnCR01kZwOhkQ4AUEwWORUxe2DwMm2j+2WtyzpBttbtUm5qjuIHwtsZ/ve3ro+j15Lys6RtAVgSS8CDgBK+9I0wr72fe8nedCvNfBPYAvb60u60/bnJR1PudjQgcD5kn5F+sysxFiMRwlfIaUknk9yAPcEXl3D/kyS5PQ7qEhO17APhkg4AUEwWJT3xvcgzY5gLMq7Lba3yr+7pvN14dE+OAAA75Y001nFrQYfIe0Nr0yqaX8l4wMNO9E0wr72fV/AaM3cn1bSd3iMsjQ7bN8iaR2SSBP0INJk+35JM3Jw4WmSbgM+XWjeD8npYEiEExAEg+UA0pfnhU6yv68Cri01zoPfKlT+V1vLvB1sWspvsySdC/w3Neu65y/uj5CW728BZko60fZxhf2eAZxou9fl96YR9o3u+wLAJZKWJa3mzM7n6tQOWJtUOXFxYCOl2gGlW0lP5/TM25VEix536xjFAAAXCUlEQVQhOWWl9ENyOhgSERgYBAsoWXRlb5Ls7DwNeNsdi+hoTPmtHS7Zm20FkuVUr41Ikfazba9f1PnUxg3Am12zgl627VuE/cKIpCWAj5Ii9E2aSX/N9jMFtocBW5OcgMuAdwE32C7Sz88ZFo+S4gEOApYhbanc39FwzP5DwAXA+qQqmEsBh9o+pcQ+GC7hBATBAMmR6f8EvJLxs/mSang/BV7XyyDaFEn3kKR6v03SKLhO0h0TRGC6tfEtUnDZRYwXHDphUqMx20YR9k3u+4KApPNIaY1n5VO7A8vYfl+B7V3ABsBttjeQtCJwlu231bj+YozFATSp+RAs4MR2QBAMlvOBr5OWcksj41vcDSxLj4F5ks6g/b56SZT2KaQyrHcAP8yzw7oxAT/PP4sAteIb+hBh3+S+LwisZ3vdyvG1kkpffys18nlJM0mfn1VKLyxpa+AM0vsvYBVJe7mwFHHWNjicMZ2BHwBH1tE6CIZHrAQEwQCRNNv2xj3abgJ8l+QMVPf0S7T3kXRbKz+/07ka/Vm0V7EdSSvllMeh0OS+LwhIOou0AnNTPn4j8DHbexbYnkxaRfl7ktz0k6QaFEVBlZJmA7vb/mk+fjWpdkPR/ZR0Aekze0Y+9Q/ABrbbVikMRks4AUEwQCQdTpqJXcj4gbyrhG1ekj8FuItKCWHb1xVeu+d9daViO6eRlqT/g1SI6BDbV5Zcu017PVcU7PF6h9PjfV8QkHQvKbjv4XxqVVL9hOdJcR1FsRlZvW+mCytPZps7J7bf7lwH+xArWoiI7YAgGCytkrb/XDlXpMEPPG373xtc+3jgx5LOJy3r7gx8odD2A7ZPlPQOYDnSbO5MUppfL3SrAtdvmtz3BYF39mooaUfg+7afsP2QpGUlvdf2fxc2MUvSfzAWj7AH46s6duNPkrayfUPuz5bUECsKhkusBATBAoqkE0iz2IsYP5vtmCI4oY3XklTnIA0MRfvKrZmfpBOBH9i+sOFWwv62T+7FNqjHJDPx4vdO0otJeg5b5VPXk7ID/jy51Tj7DUlbAcuQnL/fA3vbvqPwJQRDJJyAIBgAkt5s+/uVnP1xFObqt8tr75oi2KadFRgv3/pwh6e3bE4jifysToo0n0FyBmrts0vaCljL9mmSlicVE3qwThs1r9f4vi/sTLKcf9ew0ytzUCI9iEwFQyS2A4JgMLwJ+D5J5nYiJbK32N6m23M6IWl70pbAy0n746uR5FtfW2D+QVKK4ANZfvevSNUM61z/MJLs7Nqk+IIXkZaYt6zTTk0a3/cpwKy8ivTVfPwxxgSHJiWnFk46K+wWEyDp4EnOt+y7poYGwydWAoJgIUDSJba3rWlzBynF7mrbr5e0DfB+2x/sYLOO7fsktQ3iq7kVcTspoPBWj1URLA4wC3pDqYLiocBbSYP6VSSNhae62K3W6XHbv+hif1gX+893ejwYDbESEARDopeBvMLKPdg8Z/sxSYtIWsT2tZK+3MXmYGA/0grCRMxY3n4Jz9q2JMO8wWnoNLzvCx15sD8EQNLLbD9SaDffIC9pW9uXFNrHIL8QEk5AEAyPXgbyFrf1YPO4pKVIZXXPlvQbKsp97bC9X/7daCsic56kU4BlJe0LfAD4Rh/arUuT+76wcylJ9rlXjgCKnIB2DDs1NKhPOAFBMDx6GciBnmux70BKzTqIlOa1DOlLvSu5ANB7mF92t3hf1/a/SXobSWlwbeBztq8qte8jPd/3KUDT1MxR2wcDJpyAIBgSdQfynF99OCmgb1HSF6ptl+a6rwA8kovOnJGL0qxIKkvbjYuBZ5ggVFSXPOiPYuCv9qEXB2qq0HTl5cMN7S9taB8MmAgMDIIB0mQgl3QfaRY/m4r+ve2SQZysvb9FqwBRLgpzo+1NC2wbB/DlNL1jSM6IGHvtM5u02+Wak0W4t649bYISe0kNzXZLkuSGV7W9r6S1gLW7xQZIWhNY0faNE85vCfza9s/rvoZg8MRKQBAMlv+kzUBeyBO2L29w7UVdqUBo+9nsCJRwuaS39yoTnDkW2M72vQ3aqMu0CQCcjIapoZDSOWcDm+fjX5IKMnWLDfgy8Ok25/+YH2uXthmMmHACgmCw1B7IK+l510o6jpTb3oti4G8lbW/7otzuDsDvCm1vAi6UtAjwHL3N4h8dsgPQNY1tmnAksBkTUkNr2K9he1dJuwFknYiSvf0Vbd818aTtu3INg2ABJJyAIBgADQfyiel5m1T+rpOm9xFSVsBXSIP4HFINgBJOIM0E73LNPcOKWt8sSecC/8341z5wwR5JmwEnAa8BFiMpHj41yK2IBYheUkOrPJvjR1qpnWtQef86sGyHx5aocf1giIQTEASDoeeBvE/peeQ92M1ymiC2n6xhPge4u64DkKku+z4NvL3aLYaj2vcVUind80n3fk/g1UO47oJA7dTQCRwGfA9YRdLZJIXHvQvsZkna1/a4YERJH6JAsTAYDREYGAQLKJL+FTjW9uP5eDngE7Y/20NbtQRzJJ1Oqrh3OeNn8QuF9KukWbY3qQY4NimAtDCRRZn+BCzCWGroWa5RRjnLRG9GWkG6yXbXbSRJK5JKNz/L2KC/CWklZqdS0aJguCwy6g4EwVRG0r9KWrZyvJykowrN39VyAABs/wF4d49dqSuY8yBwDekLfOnKTzGSzmjz2r9Zsx+98nQOgrxd0rGSDmL6fN99zvZfbD9v+wynctSfqtnG4sAfSEF960r6224Gth+1vQXweeCh/PN525uHA7DgEisBQTBA2s0+S1XUJN0JbNoq4Zr3aWfZLo3yrrb1zV7z5SWtZPvXPdi1e+1DmY1nHfxHSU7MQaTZ8FenQ5pau89XnZRPSccAuwL3MKYRYdvbN+jTw7ZX7dU+GBwRExAEg2WGpBdPGMhfXGh7NnBNLusLqYrfGb10oqFgzmX0Jj27iKTl8goGkl7K8L5z3mv7RJLg0efz9Q8AThzS9YeOpI8C+wOvyg5ki6WBG9tbteW9JF2AkmDA4u71sa2gj4QTEASDpeeB3PYx+cv8LfnUkbav6GYnaR3gS6RZ3MdJFeXeC/wM2KuHtL1ev8CPB34s6fzcxs7AF3psqy57Mf+Av3ebc1OJb5NiOI4mFxDKzK0TDwA8QCr73E8nIJacF1BiOyAIBoykdzE2kF9VMpA3vN4PgeOApYAvkvaDzyUJ6Rxo+y0dzNu1t7/tk3vsy2uBVrbD923/pJd2alxvN2B3YCvg+spDM4EX6r72hZm6ioGSTiIN1isDG5BiQqpBoR/vYn/wZA8Bn7H90rKeB8MknIAgWEDpVXa3uu8u6X7ba1YeK67qJmkrYC3bp0laHljK9oM9vI6e5Gt7IccCrE6b2TBwp+3nB3XtBQVJ25F0HsYpBnaLJZG0V6fHbXdcwZJ0WBf7KDW8ABLbAUEwQBrq5/cquzuj8vfElL4i2eD8hb4JqfrfaaTl4bNIOeNF9EG+tjZZMfAXwOY5Za1VJ+He6eAAZI6iB8XA1iCfUwyfsf1CPp5BQRxLp0Fe0oGFfQ+GzHRJmQmCUXEssL3tZWzPtL10DdW6XmV3v1oRCJq3jJ8LvFxd2MaOwPZkkRnbv6JmiiBj8rU/s706aUvkpppt9ISkXYCbgV2A9wH/I2nnYVx7AeC5XGRqnmIg48WqunEN4xX+lqD8czMZk20VBCMmVgKCYLA00c/vSXbX9imTnL9f0kOF137WtiW1pGNfUmhXpal8bRM+S0qv/A1A3s64Gvh/Q7r+KGmqGLh4VV3S9pO5smATIjtgASWcgCAYLE3082fSf9ndg0kV3bpxnqRTgGUl7Qt8gPq16ZsORk1YpOUAZB5j+qx87kBKjTyIMcXAI2rYPyVpo1Z9C0kbkxQImxDBZwsoERgYBAOkkhpYxQ3z9ntG0hzbqxQ+920kB0TAFbavqnmtdvK1Z+el6oEi6VhShPs5+dSupMDAusp50w5JmwLfAX5Feu9XAna13VH/X9Jc2g/2ApawHZPOBZBwAoJgAUXS4sAHSYF01ej6nh2IYSq3SVodeMT2M/l4CVK52YeGcO1jgP8hpQpCShfcbCo7AR0GYQDqVFCU9CJSUCjAT20/17B7wQJKOAFBMECaDORZZOc+Ut77EaTZ9L22D+hi13hG1jCrodXGLGAL28/m48WAG21v2tmyOU2lcxdmJB0JPAKcSXrf9gBeZvtzNdpYD1iX8Z/Zb/W5q8ECQDgBQTBAeh3Is+1tOcXrTtvr59nZ9bY3G2yvk74AvaUnVtu43faGE87dYXuDxh2c/JrzpHOBap2ApUkOSNdUuYWddve4zn3P6aFbk5yAy4B3ATfYni7ZFdOK6RIoEwSjYk3bhwJP5Tzs9wBvLLRtLcE+nmdmy5Bm5sOgSVZDi99mrQAAJO0AdC1J25BvA9sBF+XfrZ+Np4MDkHlK0h6SZuTMjD2oF5C5Mymd89e29yHFViwziI4GoycCNYJgsEwcyH9N+UB+qqTlSOluF5FkgA/tfxfHyNsA0CyrocVHSFkBXyEtS88B/qFffW2H7SeAJ4DdBnmdBZzdSTUSWnUSbsjnSvmT7b9Iel7STJLQU1EwabDwEU5AEAyWngZySYsAf8wV+H5IWt4eBttV/m6UnpjL9m5WES56sotJ0Ady4OUODZqYJWlZUkrobOBJ4Md96FqwABIxAUEwIPJAvrPt83q0n2W7jtLbAoukS2xvO+p+TDfq1IqYxP6VwEzbd3Z5arCQEjEBQTAgbP8F+GSDJq6W9E+SVpH00tZPv/rXCUln5Nlg63g5Sd9s0OTKfehWUJ/aSn2SdpS0DMxbVXhY0nv73bFgwSBWAoJggEj6IikY7lwqwVkl9d0ltavYZ9sD3xqoViLsdK5Ge98clUDSdEbSUbY/W9OmXVZHz+99sGATTkAQDJA8kM/3TzaMgbwJku4Ats4xCeQViOtsv260PQsGTTs9BUl3xXs/NYntgCAYLOsCXwXuAG4HTqKwlK6kxSUdLOm/JF0g6cAsPjQMjgd+LOlISUcBPyJVROyKpHUkXS7pUklrSDpd0uOSbpb0moH2OmiLpLtqPH2WpBPye7eGpBNIAYLBFCRWAoJggEg6D/gjcHY+tTuwjO33FdrOBc6q2C5re5dB9LXN9V8LbJMPv2/7J4V2PwSOI2VCfBH4FGk7ZFvgQNtvGUB3pz2V9M75HgK+bnv5wnZeQspgeStpFesq4Au2h1X8KRgi4QQEwQCR9BPb63Y712/bfiFpBcZLxz5cYDNv/1jS/bbXrDzWKFo9mBxJz5GczXZf6jvbXrqHNl9m+5HGnQsWWEInIAgGy62SNrN9E4CkNwKzhmDbiKz0dzzwcpJYzGrAvZRtZcyo/H3ChMcW60sHg3bcCfyb7bsnPiDprT22eSkQTtsUJpyAIBgAeQ/WwIuAH0l6OB+vRqolMBDbPnIksBlwda5fsA1QKrv7VUlL2X7S9smtk5LWBK4eQF+DxIGkrad27Nhjm7VTDIOFi3ACgmAwNBHGWRBEdZ6z/VjWnl/E9rWSvlxiaPuUSc7fL+mhvvYymIft6zs8vBW9rSJ9o8fuBAsJERMQBMF8SLoaeC9wNPDXpC2BTW1v0bDdh22v2ocuBjWoe98lzQBWpDJRLIkHCRY+wgkIgmA+coT4n0hpxHuQqsidbfuxhu3OsR3FaIZMnfsu6R+Bw4BHgRdIWwKeqB0QTA1iOyAIgnasADxi+xngDElLkGaGjZwA2keuB4Onzn0/AFi7qcMXLByEExAEQTvOB6pL/y/kc5t2M5Q0l/aDjoAl+tK7YD76eN/nkMoxB9OAcAKCIGjHorafbR3YflZSUXpfL/noQXP6eN8fAH4g6VLgz5X2J6Z7BlOAkA0OgqAdv81aAQBI2oFUCCmY+jxMUglcDFi68hNMQSIwMAiC+ZC0Bkl97uWk5eQ5wD/Y/vlIOxYMDUlLAdh+ctR9CQZHOAFBEExKDATTD0nrAWcCL82nfgfsafue0fUqGBSxHRAEwaTkwf87o+5HMFROBQ62vZrt1YBPEKJBU5ZwAoIg6MbKo+5AMFReYvva1oHtHwAvGV13gkES2QFBEHTjtlF3IBgqD0g6lLQlAKlmxAMj7E8wQCImIAiCIJiHpOWAz5PqDQBcDxxu+w+j61UwKMIJCIJgHpLWAb4E/AX4OHAoqYbAz4C9bN87wu4FQdBnYjsgCIIqpwLHAUsB3wc+BexDqmz4FeAto+taMEgkfdn2gZIupo3yoO3t25gFCzmxEhAEwTwk3Wb79fnv+22vWXnsVtsbja53wSCRtLHt2ZLe1O5x29cNu0/B4ImVgCAIqsyo/D1RJrZINjhYOLE9O/+5oe0Tq49JOgAIJ2AKEimCQRBU+WpFIOjk1klJawJXj6xXwTDZq825vYfdiWA4xEpAEATzsH3KJOfvl/TQkLsTDBFJuwG7A6tLuqjy0NLA70fTq2DQRExAEARFSHrY9qqj7kcwGCStBqwOHA0cUnloLnCn7edH0rFgoIQTEARBEZLm2F5l1P0IBoukVwG/sv1MPl4CWNH2QyPtWDAQIiYgCIJSYsYwPTiPpBPR4gXg/BH1JRgwERMQBME8JM2l/WAvYIkhdycYDYvafrZ1YPtZSZEZMkUJJyAIgnnYXnrUfQhGzm8lbW/7IgBJO5DKCQdTkIgJCIIgCOYhaQ3gbODlpBWgOcCetu8faceCgRBOQBAEQTAfFb2IJ0fdl2BwhBMQBEEQzEPS59qdt33EsPsSDJ6ICQiCIAiqPFX5e3FS8aioHjlFiZWAIAiCYFIkvRi4wvbWo+5L0H9CJyAIgiDoxJLAK0bdiWAwxHZAEARBMA9JdzGmFTEDWB6IeIApSmwHBEEQBPPINQRaPA88GnUDpi7hBARBEAQASJoB3GN7nVH3JRgOERMQBEEQAGD7BeCnkqJa5DQhYgKCIAiCKssB90i6mUq6oO3tR9elYFCEExAEQRBUOXTUHQiGRzgBQRAEQZV32/5U9YSkY4DrRtSfYIBETEAQBEFQ5W1tzr1r6L0IhkKsBARBEARI+iiwP7CGpDsrDy0N3DiaXgWDJlIEgyAIAiQtQwoKPBo4pPLQXNu/H02vgkETTkAQBEEwD0lrAP9n+8+StgbWB75l+/HR9iwYBBETEARBEFS5AHhB0prAqcAqwLdH26VgUIQTEARBEFT5S5YJ3gk4yfY/Ay8bcZ+CARFOQBAEQVDlOUm7AXsCl+RzLxphf4IBEk5AEARBUGUfYHPgC7YflLQ6cOaI+xQMiAgMDIIgCIJpSugEBEEQBPOQtCVwOLAaaYwQYNuvGmW/gsEQKwFBEATBPCTdBxwEzAZeaJ23/djIOhUMjFgJCIIgCKo8YfvyUXciGA6xEhAEQRDMQ9IXgRnAfwF/bp23fevIOhUMjHACgiAIgnlIurbNadt+89A7EwyccAKCIAiCYJoSMQFBEAQBkt5v+yxJB7d73PYJw+5TMHjCCQiCIAgAXpJ/Lz3SXgRDJbYDgiAIgmCaErLBQRAEQVskRUbAFCecgCAIgmAyNOoOBIMlnIAgCIJgMi4ddQeCwRIxAUEQBEEwTYmVgCAIgqAjku4adR+CwRApgkEQBAGSdprsIWClYfYlGB7hBARBEAQA5wJnA+32iBcfcl+CIRExAUEQBAGSZgN72b67zWNzbK8ygm4FAyZiAoIgCAKAA4E/TvLYjsPsSDA8wgkIgiAIsH297YcneXiroXYmGBqxHRAEQRB0RNLDtlcddT+C/hMrAUEQBEE3QjlwihJOQBAEQdCNWDKeokSKYBAEQYCkubQf7AUsMeTuBEMiYgKCIAiCYJoS2wFBEARBME0JJyAIgiAIpinhBARBEATBNCWcgCAIgiCYpoQTEARBEATTlP8PL740QhH3RaMAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "corr_matrix = df.corr()\n",
        "sns.heatmap(corr_matrix);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VsZCWdVO1eQ-",
        "outputId": "16406a46-89b2-42d8-dfc8-e797c454f79e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "k = 17\n",
        "cols = corr_matrix.nlargest(k, 'Speedup').index\n",
        "cm = np.corrcoef(df[cols].values.T)\n",
        "sns.set(font_scale=1.25)\n",
        "fig, ax = plt.subplots(figsize=(15,15))  \n",
        "hm = sns.heatmap(cm, cmap=\"Greens\", annot=True, fmt='.2f', annot_kws={'size': 15}, yticklabels=cols.values, xticklabels=cols.values, ax=ax)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "yV0q0BXoz8bh"
      },
      "outputs": [],
      "source": [
        "# pca = PCA(n_components = 10)\n",
        "# pca = pca.fit(X)\n",
        "# pca_ = pca.transform(X, columns = X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZRMiODsD_hC",
        "outputId": "fbb0c110-ea4a-4635-d4b9-c932f5395d1c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['L3-cache-miss-rate in %', 'LLC-load-misses', 'L3-cache-misses',\n",
              "       'L3-cache-references', 'Threads'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cols[1:6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6F8fpnnu4nN_",
        "outputId": "97f92c5f-9f2a-465b-ac1e-c3a962dff85d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>L3-cache-miss-rate in %</th>\n",
              "      <th>LLC-load-misses</th>\n",
              "      <th>L3-cache-misses</th>\n",
              "      <th>L3-cache-references</th>\n",
              "      <th>Threads</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.449487</td>\n",
              "      <td>9083164</td>\n",
              "      <td>9047670</td>\n",
              "      <td>86562301</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.800576</td>\n",
              "      <td>9258039</td>\n",
              "      <td>8872102</td>\n",
              "      <td>90521796</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.379151</td>\n",
              "      <td>9107829</td>\n",
              "      <td>9746879</td>\n",
              "      <td>93909804</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.457418</td>\n",
              "      <td>83345</td>\n",
              "      <td>138506</td>\n",
              "      <td>402702</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.136830</td>\n",
              "      <td>9114520</td>\n",
              "      <td>9253721</td>\n",
              "      <td>179880974</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>10.580639</td>\n",
              "      <td>9229494</td>\n",
              "      <td>9722742</td>\n",
              "      <td>91879273</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>9.873712</td>\n",
              "      <td>8522963</td>\n",
              "      <td>8990271</td>\n",
              "      <td>90822200</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>9.846910</td>\n",
              "      <td>7963788</td>\n",
              "      <td>8443100</td>\n",
              "      <td>85780500</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>327</th>\n",
              "      <td>10.563879</td>\n",
              "      <td>9235674</td>\n",
              "      <td>9749467</td>\n",
              "      <td>92273773</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>8.926301</td>\n",
              "      <td>7311439</td>\n",
              "      <td>7777621</td>\n",
              "      <td>87136476</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>329 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     L3-cache-miss-rate in %  LLC-load-misses  ...  L3-cache-references  Threads\n",
              "0                  10.449487          9083164  ...             86562301        7\n",
              "1                   9.800576          9258039  ...             90521796        7\n",
              "2                  10.379151          9107829  ...             93909804        7\n",
              "3                  35.457418            83345  ...               402702        8\n",
              "4                   5.136830          9114520  ...            179880974        2\n",
              "..                       ...              ...  ...                  ...      ...\n",
              "324                10.580639          9229494  ...             91879273        8\n",
              "325                 9.873712          8522963  ...             90822200        8\n",
              "326                 9.846910          7963788  ...             85780500        6\n",
              "327                10.563879          9235674  ...             92273773        7\n",
              "328                 8.926301          7311439  ...             87136476        4\n",
              "\n",
              "[329 rows x 5 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[cols[1:6]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "id": "9YakMzeQo4Zr",
        "outputId": "34c1ab1e-9d61-48dd-ce02-8490552a57c3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fecade5d610>"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(df[cols[0:5]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "UVuyJvYapDOh",
        "outputId": "bf94b727-7607-4652-8f89-3b2cc1c66741"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threads</th>\n",
              "      <th>Size</th>\n",
              "      <th>branch-instructions</th>\n",
              "      <th>branch-misses in %</th>\n",
              "      <th>L3-cache-misses</th>\n",
              "      <th>L3-cache-miss-rate in %</th>\n",
              "      <th>L3-cache-references</th>\n",
              "      <th>cpu-cycles</th>\n",
              "      <th>total-instructions</th>\n",
              "      <th>IPC</th>\n",
              "      <th>cpu-clock</th>\n",
              "      <th>page-faults</th>\n",
              "      <th>L1-data-cache-loads</th>\n",
              "      <th>L1-instruction-cache-load-misses</th>\n",
              "      <th>LLC-load-misses</th>\n",
              "      <th>Exe-time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.446636</td>\n",
              "      <td>1.060636</td>\n",
              "      <td>-0.375865</td>\n",
              "      <td>1.038565</td>\n",
              "      <td>-0.134563</td>\n",
              "      <td>-0.329654</td>\n",
              "      <td>-0.131487</td>\n",
              "      <td>-0.160461</td>\n",
              "      <td>-0.271233</td>\n",
              "      <td>-0.768126</td>\n",
              "      <td>-0.160232</td>\n",
              "      <td>0.257209</td>\n",
              "      <td>-0.230083</td>\n",
              "      <td>0.474806</td>\n",
              "      <td>-0.133920</td>\n",
              "      <td>-0.320181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.446636</td>\n",
              "      <td>1.210202</td>\n",
              "      <td>-0.377614</td>\n",
              "      <td>1.105175</td>\n",
              "      <td>-0.134747</td>\n",
              "      <td>-0.369756</td>\n",
              "      <td>-0.129447</td>\n",
              "      <td>-0.162224</td>\n",
              "      <td>-0.270362</td>\n",
              "      <td>-0.668892</td>\n",
              "      <td>-0.162201</td>\n",
              "      <td>0.208711</td>\n",
              "      <td>-0.229271</td>\n",
              "      <td>0.580988</td>\n",
              "      <td>-0.133735</td>\n",
              "      <td>-0.320339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.446636</td>\n",
              "      <td>-1.088063</td>\n",
              "      <td>-0.371858</td>\n",
              "      <td>0.973620</td>\n",
              "      <td>-0.133832</td>\n",
              "      <td>-0.334001</td>\n",
              "      <td>-0.127701</td>\n",
              "      <td>-0.160541</td>\n",
              "      <td>-0.270777</td>\n",
              "      <td>-0.766657</td>\n",
              "      <td>-0.160457</td>\n",
              "      <td>0.256604</td>\n",
              "      <td>-0.229194</td>\n",
              "      <td>0.606329</td>\n",
              "      <td>-0.133894</td>\n",
              "      <td>-0.315975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.700424</td>\n",
              "      <td>1.210202</td>\n",
              "      <td>-0.442473</td>\n",
              "      <td>-1.545831</td>\n",
              "      <td>-0.143883</td>\n",
              "      <td>1.215819</td>\n",
              "      <td>-0.175875</td>\n",
              "      <td>-0.182222</td>\n",
              "      <td>-0.300135</td>\n",
              "      <td>0.078696</td>\n",
              "      <td>-0.181406</td>\n",
              "      <td>-0.861900</td>\n",
              "      <td>-0.259309</td>\n",
              "      <td>-1.060430</td>\n",
              "      <td>-0.143420</td>\n",
              "      <td>0.010764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.822304</td>\n",
              "      <td>-0.055210</td>\n",
              "      <td>-0.082852</td>\n",
              "      <td>0.003833</td>\n",
              "      <td>-0.134348</td>\n",
              "      <td>-0.657973</td>\n",
              "      <td>-0.083410</td>\n",
              "      <td>-0.101599</td>\n",
              "      <td>-0.103405</td>\n",
              "      <td>-0.123985</td>\n",
              "      <td>-0.101187</td>\n",
              "      <td>0.264873</td>\n",
              "      <td>-0.090815</td>\n",
              "      <td>0.906533</td>\n",
              "      <td>-0.133887</td>\n",
              "      <td>-0.164733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Threads      Size  ...  LLC-load-misses  Exe-time\n",
              "0  0.446636  1.060636  ...        -0.133920 -0.320181\n",
              "1  0.446636  1.210202  ...        -0.133735 -0.320339\n",
              "2  0.446636 -1.088063  ...        -0.133894 -0.315975\n",
              "3  0.700424  1.210202  ...        -0.143420  0.010764\n",
              "4 -0.822304 -0.055210  ...        -0.133887 -0.164733\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scaler = StandardScaler().fit(X)\n",
        "X_standardized = pd.DataFrame(scaler.transform(X),\n",
        "                              columns = X.columns)\n",
        "X_standardized.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "oN6KSLOu1Sby"
      },
      "outputs": [],
      "source": [
        "X_train_std, X_test_std, Y_train_std, Y_test_std = train_test_split(X_standardized, Y, test_size=0.2, random_state=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "7JwP3GBop-gU"
      },
      "outputs": [],
      "source": [
        "ANN_model = Sequential()\n",
        "\n",
        "ANN_model.add(Dense(16, kernel_initializer='normal',input_dim = X_train_std.shape[1], activation='relu'))\n",
        "\n",
        "ANN_model.add(Dense(16, kernel_initializer='normal',activation='relu'))\n",
        "ANN_model.add(Dense(8, kernel_initializer='normal',activation='relu'))\n",
        "ANN_model.add(Dense(8, kernel_initializer='normal',activation='relu'))\n",
        "\n",
        "ANN_model.add(Dense(1, kernel_initializer='normal'))\n",
        "\n",
        "ANN_model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obgxzljWyDyo",
        "outputId": "5b986543-9270-4944-c880-1e9e987e27fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/7 [===>..........................] - ETA: 6s - loss: 3.1402 - mean_absolute_error: 3.1402\n",
            "Epoch 00001: val_loss improved from inf to 2.21076, saving model to Weights-001--2.2108.hdf5\n",
            "7/7 [==============================] - 1s 44ms/step - loss: 2.2915 - mean_absolute_error: 2.2915 - val_loss: 2.2108 - val_mean_absolute_error: 2.2108\n",
            "Epoch 2/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8365 - mean_absolute_error: 1.8365\n",
            "Epoch 00002: val_loss improved from 2.21076 to 2.20288, saving model to Weights-002--2.2029.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 2.2839 - mean_absolute_error: 2.2839 - val_loss: 2.2029 - val_mean_absolute_error: 2.2029\n",
            "Epoch 3/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.8933 - mean_absolute_error: 2.8933\n",
            "Epoch 00003: val_loss improved from 2.20288 to 2.19440, saving model to Weights-003--2.1944.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 2.2758 - mean_absolute_error: 2.2758 - val_loss: 2.1944 - val_mean_absolute_error: 2.1944\n",
            "Epoch 4/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.2275 - mean_absolute_error: 2.2275\n",
            "Epoch 00004: val_loss improved from 2.19440 to 2.18508, saving model to Weights-004--2.1851.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 2.2670 - mean_absolute_error: 2.2670 - val_loss: 2.1851 - val_mean_absolute_error: 2.1851\n",
            "Epoch 5/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.2538 - mean_absolute_error: 2.2538\n",
            "Epoch 00005: val_loss improved from 2.18508 to 2.17453, saving model to Weights-005--2.1745.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 2.2572 - mean_absolute_error: 2.2572 - val_loss: 2.1745 - val_mean_absolute_error: 2.1745\n",
            "Epoch 6/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.7130 - mean_absolute_error: 2.7130\n",
            "Epoch 00006: val_loss improved from 2.17453 to 2.16213, saving model to Weights-006--2.1621.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 2.2459 - mean_absolute_error: 2.2459 - val_loss: 2.1621 - val_mean_absolute_error: 2.1621\n",
            "Epoch 7/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.2764 - mean_absolute_error: 2.2764\n",
            "Epoch 00007: val_loss improved from 2.16213 to 2.14686, saving model to Weights-007--2.1469.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 2.2322 - mean_absolute_error: 2.2322 - val_loss: 2.1469 - val_mean_absolute_error: 2.1469\n",
            "Epoch 8/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 3.7913 - mean_absolute_error: 3.7913\n",
            "Epoch 00008: val_loss improved from 2.14686 to 2.12701, saving model to Weights-008--2.1270.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 2.2148 - mean_absolute_error: 2.2148 - val_loss: 2.1270 - val_mean_absolute_error: 2.1270\n",
            "Epoch 9/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.1283 - mean_absolute_error: 2.1283\n",
            "Epoch 00009: val_loss improved from 2.12701 to 2.09978, saving model to Weights-009--2.0998.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 2.1911 - mean_absolute_error: 2.1911 - val_loss: 2.0998 - val_mean_absolute_error: 2.0998\n",
            "Epoch 10/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.5707 - mean_absolute_error: 2.5707\n",
            "Epoch 00010: val_loss improved from 2.09978 to 2.06151, saving model to Weights-010--2.0615.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 2.1577 - mean_absolute_error: 2.1577 - val_loss: 2.0615 - val_mean_absolute_error: 2.0615\n",
            "Epoch 11/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3290 - mean_absolute_error: 1.3290\n",
            "Epoch 00011: val_loss improved from 2.06151 to 2.00785, saving model to Weights-011--2.0079.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 2.1087 - mean_absolute_error: 2.1087 - val_loss: 2.0079 - val_mean_absolute_error: 2.0079\n",
            "Epoch 12/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.0173 - mean_absolute_error: 2.0173\n",
            "Epoch 00012: val_loss improved from 2.00785 to 1.93314, saving model to Weights-012--1.9331.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 2.0404 - mean_absolute_error: 2.0404 - val_loss: 1.9331 - val_mean_absolute_error: 1.9331\n",
            "Epoch 13/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8834 - mean_absolute_error: 1.8834\n",
            "Epoch 00013: val_loss improved from 1.93314 to 1.83552, saving model to Weights-013--1.8355.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.9493 - mean_absolute_error: 1.9493 - val_loss: 1.8355 - val_mean_absolute_error: 1.8355\n",
            "Epoch 14/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7807 - mean_absolute_error: 1.7807\n",
            "Epoch 00014: val_loss improved from 1.83552 to 1.72488, saving model to Weights-014--1.7249.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.8611 - mean_absolute_error: 1.8611 - val_loss: 1.7249 - val_mean_absolute_error: 1.7249\n",
            "Epoch 15/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7131 - mean_absolute_error: 1.7131\n",
            "Epoch 00015: val_loss improved from 1.72488 to 1.60146, saving model to Weights-015--1.6015.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.7791 - mean_absolute_error: 1.7791 - val_loss: 1.6015 - val_mean_absolute_error: 1.6015\n",
            "Epoch 16/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4491 - mean_absolute_error: 1.4491\n",
            "Epoch 00016: val_loss improved from 1.60146 to 1.51564, saving model to Weights-016--1.5156.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.7210 - mean_absolute_error: 1.7210 - val_loss: 1.5156 - val_mean_absolute_error: 1.5156\n",
            "Epoch 17/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7150 - mean_absolute_error: 1.7150\n",
            "Epoch 00017: val_loss improved from 1.51564 to 1.49375, saving model to Weights-017--1.4938.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.6936 - mean_absolute_error: 1.6936 - val_loss: 1.4938 - val_mean_absolute_error: 1.4938\n",
            "Epoch 18/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2777 - mean_absolute_error: 1.2777\n",
            "Epoch 00018: val_loss improved from 1.49375 to 1.48743, saving model to Weights-018--1.4874.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.6635 - mean_absolute_error: 1.6635 - val_loss: 1.4874 - val_mean_absolute_error: 1.4874\n",
            "Epoch 19/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2875 - mean_absolute_error: 1.2875\n",
            "Epoch 00019: val_loss improved from 1.48743 to 1.48218, saving model to Weights-019--1.4822.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.6401 - mean_absolute_error: 1.6401 - val_loss: 1.4822 - val_mean_absolute_error: 1.4822\n",
            "Epoch 20/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.9969 - mean_absolute_error: 1.9969\n",
            "Epoch 00020: val_loss did not improve from 1.48218\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.6227 - mean_absolute_error: 1.6227 - val_loss: 1.4852 - val_mean_absolute_error: 1.4852\n",
            "Epoch 21/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8893 - mean_absolute_error: 1.8893\n",
            "Epoch 00021: val_loss did not improve from 1.48218\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.6075 - mean_absolute_error: 1.6075 - val_loss: 1.4831 - val_mean_absolute_error: 1.4831\n",
            "Epoch 22/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0933 - mean_absolute_error: 1.0933\n",
            "Epoch 00022: val_loss improved from 1.48218 to 1.47468, saving model to Weights-022--1.4747.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5969 - mean_absolute_error: 1.5969 - val_loss: 1.4747 - val_mean_absolute_error: 1.4747\n",
            "Epoch 23/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6026 - mean_absolute_error: 1.6026\n",
            "Epoch 00023: val_loss improved from 1.47468 to 1.46565, saving model to Weights-023--1.4657.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.5882 - mean_absolute_error: 1.5882 - val_loss: 1.4657 - val_mean_absolute_error: 1.4657\n",
            "Epoch 24/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6016 - mean_absolute_error: 1.6016\n",
            "Epoch 00024: val_loss improved from 1.46565 to 1.45872, saving model to Weights-024--1.4587.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.5808 - mean_absolute_error: 1.5808 - val_loss: 1.4587 - val_mean_absolute_error: 1.4587\n",
            "Epoch 25/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.9024 - mean_absolute_error: 1.9024\n",
            "Epoch 00025: val_loss improved from 1.45872 to 1.45642, saving model to Weights-025--1.4564.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5733 - mean_absolute_error: 1.5733 - val_loss: 1.4564 - val_mean_absolute_error: 1.4564\n",
            "Epoch 26/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8969 - mean_absolute_error: 1.8969\n",
            "Epoch 00026: val_loss improved from 1.45642 to 1.45265, saving model to Weights-026--1.4527.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5694 - mean_absolute_error: 1.5694 - val_loss: 1.4527 - val_mean_absolute_error: 1.4527\n",
            "Epoch 27/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2312 - mean_absolute_error: 1.2312\n",
            "Epoch 00027: val_loss improved from 1.45265 to 1.44798, saving model to Weights-027--1.4480.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5633 - mean_absolute_error: 1.5633 - val_loss: 1.4480 - val_mean_absolute_error: 1.4480\n",
            "Epoch 28/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8441 - mean_absolute_error: 1.8441\n",
            "Epoch 00028: val_loss improved from 1.44798 to 1.44017, saving model to Weights-028--1.4402.hdf5\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 1.5588 - mean_absolute_error: 1.5588 - val_loss: 1.4402 - val_mean_absolute_error: 1.4402\n",
            "Epoch 29/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0714 - mean_absolute_error: 1.0714\n",
            "Epoch 00029: val_loss improved from 1.44017 to 1.43559, saving model to Weights-029--1.4356.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5556 - mean_absolute_error: 1.5556 - val_loss: 1.4356 - val_mean_absolute_error: 1.4356\n",
            "Epoch 30/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8082 - mean_absolute_error: 1.8082\n",
            "Epoch 00030: val_loss improved from 1.43559 to 1.43456, saving model to Weights-030--1.4346.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5514 - mean_absolute_error: 1.5514 - val_loss: 1.4346 - val_mean_absolute_error: 1.4346\n",
            "Epoch 31/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7170 - mean_absolute_error: 0.7170\n",
            "Epoch 00031: val_loss improved from 1.43456 to 1.43044, saving model to Weights-031--1.4304.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5478 - mean_absolute_error: 1.5478 - val_loss: 1.4304 - val_mean_absolute_error: 1.4304\n",
            "Epoch 32/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4413 - mean_absolute_error: 1.4413\n",
            "Epoch 00032: val_loss improved from 1.43044 to 1.42426, saving model to Weights-032--1.4243.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.5456 - mean_absolute_error: 1.5456 - val_loss: 1.4243 - val_mean_absolute_error: 1.4243\n",
            "Epoch 33/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7885 - mean_absolute_error: 1.7885\n",
            "Epoch 00033: val_loss improved from 1.42426 to 1.41902, saving model to Weights-033--1.4190.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5413 - mean_absolute_error: 1.5413 - val_loss: 1.4190 - val_mean_absolute_error: 1.4190\n",
            "Epoch 34/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5252 - mean_absolute_error: 1.5252\n",
            "Epoch 00034: val_loss improved from 1.41902 to 1.41648, saving model to Weights-034--1.4165.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5379 - mean_absolute_error: 1.5379 - val_loss: 1.4165 - val_mean_absolute_error: 1.4165\n",
            "Epoch 35/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3054 - mean_absolute_error: 1.3054\n",
            "Epoch 00035: val_loss improved from 1.41648 to 1.41435, saving model to Weights-035--1.4144.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5339 - mean_absolute_error: 1.5339 - val_loss: 1.4144 - val_mean_absolute_error: 1.4144\n",
            "Epoch 36/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8804 - mean_absolute_error: 1.8804\n",
            "Epoch 00036: val_loss did not improve from 1.41435\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5325 - mean_absolute_error: 1.5325 - val_loss: 1.4149 - val_mean_absolute_error: 1.4149\n",
            "Epoch 37/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8729 - mean_absolute_error: 1.8729\n",
            "Epoch 00037: val_loss improved from 1.41435 to 1.41364, saving model to Weights-037--1.4136.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5311 - mean_absolute_error: 1.5311 - val_loss: 1.4136 - val_mean_absolute_error: 1.4136\n",
            "Epoch 38/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5898 - mean_absolute_error: 0.5898\n",
            "Epoch 00038: val_loss improved from 1.41364 to 1.41229, saving model to Weights-038--1.4123.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5274 - mean_absolute_error: 1.5274 - val_loss: 1.4123 - val_mean_absolute_error: 1.4123\n",
            "Epoch 39/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6746 - mean_absolute_error: 1.6746\n",
            "Epoch 00039: val_loss improved from 1.41229 to 1.40961, saving model to Weights-039--1.4096.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5276 - mean_absolute_error: 1.5276 - val_loss: 1.4096 - val_mean_absolute_error: 1.4096\n",
            "Epoch 40/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7433 - mean_absolute_error: 1.7433\n",
            "Epoch 00040: val_loss improved from 1.40961 to 1.40649, saving model to Weights-040--1.4065.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5275 - mean_absolute_error: 1.5275 - val_loss: 1.4065 - val_mean_absolute_error: 1.4065\n",
            "Epoch 41/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6889 - mean_absolute_error: 1.6889\n",
            "Epoch 00041: val_loss did not improve from 1.40649\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 1.5243 - mean_absolute_error: 1.5243 - val_loss: 1.4071 - val_mean_absolute_error: 1.4071\n",
            "Epoch 42/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0540 - mean_absolute_error: 1.0540\n",
            "Epoch 00042: val_loss did not improve from 1.40649\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5251 - mean_absolute_error: 1.5251 - val_loss: 1.4071 - val_mean_absolute_error: 1.4071\n",
            "Epoch 43/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6173 - mean_absolute_error: 1.6173\n",
            "Epoch 00043: val_loss improved from 1.40649 to 1.40445, saving model to Weights-043--1.4044.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.5220 - mean_absolute_error: 1.5220 - val_loss: 1.4044 - val_mean_absolute_error: 1.4044\n",
            "Epoch 44/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.2719 - mean_absolute_error: 2.2719\n",
            "Epoch 00044: val_loss improved from 1.40445 to 1.40374, saving model to Weights-044--1.4037.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5216 - mean_absolute_error: 1.5216 - val_loss: 1.4037 - val_mean_absolute_error: 1.4037\n",
            "Epoch 45/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6917 - mean_absolute_error: 1.6917\n",
            "Epoch 00045: val_loss did not improve from 1.40374\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5217 - mean_absolute_error: 1.5217 - val_loss: 1.4075 - val_mean_absolute_error: 1.4075\n",
            "Epoch 46/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3251 - mean_absolute_error: 1.3251\n",
            "Epoch 00046: val_loss did not improve from 1.40374\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 1.5220 - mean_absolute_error: 1.5220 - val_loss: 1.4073 - val_mean_absolute_error: 1.4073\n",
            "Epoch 47/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7877 - mean_absolute_error: 0.7877\n",
            "Epoch 00047: val_loss improved from 1.40374 to 1.39924, saving model to Weights-047--1.3992.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5243 - mean_absolute_error: 1.5243 - val_loss: 1.3992 - val_mean_absolute_error: 1.3992\n",
            "Epoch 48/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4255 - mean_absolute_error: 1.4255\n",
            "Epoch 00048: val_loss did not improve from 1.39924\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5200 - mean_absolute_error: 1.5200 - val_loss: 1.4004 - val_mean_absolute_error: 1.4004\n",
            "Epoch 49/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6457 - mean_absolute_error: 1.6457\n",
            "Epoch 00049: val_loss did not improve from 1.39924\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5203 - mean_absolute_error: 1.5203 - val_loss: 1.3996 - val_mean_absolute_error: 1.3996\n",
            "Epoch 50/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6979 - mean_absolute_error: 1.6979\n",
            "Epoch 00050: val_loss did not improve from 1.39924\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5216 - mean_absolute_error: 1.5216 - val_loss: 1.4067 - val_mean_absolute_error: 1.4067\n",
            "Epoch 51/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2200 - mean_absolute_error: 1.2200\n",
            "Epoch 00051: val_loss did not improve from 1.39924\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5189 - mean_absolute_error: 1.5189 - val_loss: 1.4088 - val_mean_absolute_error: 1.4088\n",
            "Epoch 52/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5359 - mean_absolute_error: 1.5359\n",
            "Epoch 00052: val_loss did not improve from 1.39924\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5185 - mean_absolute_error: 1.5185 - val_loss: 1.4042 - val_mean_absolute_error: 1.4042\n",
            "Epoch 53/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.9083 - mean_absolute_error: 1.9083\n",
            "Epoch 00053: val_loss improved from 1.39924 to 1.39818, saving model to Weights-053--1.3982.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.5150 - mean_absolute_error: 1.5150 - val_loss: 1.3982 - val_mean_absolute_error: 1.3982\n",
            "Epoch 54/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7315 - mean_absolute_error: 0.7315\n",
            "Epoch 00054: val_loss did not improve from 1.39818\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5159 - mean_absolute_error: 1.5159 - val_loss: 1.4015 - val_mean_absolute_error: 1.4015\n",
            "Epoch 55/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7850 - mean_absolute_error: 1.7850\n",
            "Epoch 00055: val_loss did not improve from 1.39818\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5131 - mean_absolute_error: 1.5131 - val_loss: 1.4011 - val_mean_absolute_error: 1.4011\n",
            "Epoch 56/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.0326 - mean_absolute_error: 2.0326\n",
            "Epoch 00056: val_loss did not improve from 1.39818\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5128 - mean_absolute_error: 1.5128 - val_loss: 1.3999 - val_mean_absolute_error: 1.3999\n",
            "Epoch 57/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7947 - mean_absolute_error: 0.7947\n",
            "Epoch 00057: val_loss improved from 1.39818 to 1.39594, saving model to Weights-057--1.3959.hdf5\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 1.5108 - mean_absolute_error: 1.5108 - val_loss: 1.3959 - val_mean_absolute_error: 1.3959\n",
            "Epoch 58/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4921 - mean_absolute_error: 1.4921\n",
            "Epoch 00058: val_loss did not improve from 1.39594\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5119 - mean_absolute_error: 1.5119 - val_loss: 1.3999 - val_mean_absolute_error: 1.3999\n",
            "Epoch 59/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8363 - mean_absolute_error: 1.8363\n",
            "Epoch 00059: val_loss did not improve from 1.39594\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5144 - mean_absolute_error: 1.5144 - val_loss: 1.4097 - val_mean_absolute_error: 1.4097\n",
            "Epoch 60/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2410 - mean_absolute_error: 1.2410\n",
            "Epoch 00060: val_loss did not improve from 1.39594\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5141 - mean_absolute_error: 1.5141 - val_loss: 1.4005 - val_mean_absolute_error: 1.4005\n",
            "Epoch 61/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5238 - mean_absolute_error: 1.5238\n",
            "Epoch 00061: val_loss improved from 1.39594 to 1.39396, saving model to Weights-061--1.3940.hdf5\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 1.5192 - mean_absolute_error: 1.5192 - val_loss: 1.3940 - val_mean_absolute_error: 1.3940\n",
            "Epoch 62/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8021 - mean_absolute_error: 0.8021\n",
            "Epoch 00062: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5117 - mean_absolute_error: 1.5117 - val_loss: 1.3969 - val_mean_absolute_error: 1.3969\n",
            "Epoch 63/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2920 - mean_absolute_error: 1.2920\n",
            "Epoch 00063: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5077 - mean_absolute_error: 1.5077 - val_loss: 1.4001 - val_mean_absolute_error: 1.4001\n",
            "Epoch 64/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.6086 - mean_absolute_error: 2.6086\n",
            "Epoch 00064: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5079 - mean_absolute_error: 1.5079 - val_loss: 1.4010 - val_mean_absolute_error: 1.4010\n",
            "Epoch 65/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3959 - mean_absolute_error: 1.3959\n",
            "Epoch 00065: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5085 - mean_absolute_error: 1.5085 - val_loss: 1.4002 - val_mean_absolute_error: 1.4002\n",
            "Epoch 66/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3492 - mean_absolute_error: 1.3492\n",
            "Epoch 00066: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5069 - mean_absolute_error: 1.5069 - val_loss: 1.3982 - val_mean_absolute_error: 1.3982\n",
            "Epoch 67/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5446 - mean_absolute_error: 1.5446\n",
            "Epoch 00067: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5051 - mean_absolute_error: 1.5051 - val_loss: 1.3962 - val_mean_absolute_error: 1.3962\n",
            "Epoch 68/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3539 - mean_absolute_error: 1.3539\n",
            "Epoch 00068: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 1.5049 - mean_absolute_error: 1.5049 - val_loss: 1.3946 - val_mean_absolute_error: 1.3946\n",
            "Epoch 69/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7693 - mean_absolute_error: 0.7693\n",
            "Epoch 00069: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5032 - mean_absolute_error: 1.5032 - val_loss: 1.4001 - val_mean_absolute_error: 1.4001\n",
            "Epoch 70/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.0559 - mean_absolute_error: 2.0559\n",
            "Epoch 00070: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 1.5054 - mean_absolute_error: 1.5054 - val_loss: 1.3994 - val_mean_absolute_error: 1.3994\n",
            "Epoch 71/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2655 - mean_absolute_error: 1.2655\n",
            "Epoch 00071: val_loss did not improve from 1.39396\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.5028 - mean_absolute_error: 1.5028 - val_loss: 1.3951 - val_mean_absolute_error: 1.3951\n",
            "Epoch 72/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1067 - mean_absolute_error: 1.1067\n",
            "Epoch 00072: val_loss improved from 1.39396 to 1.39336, saving model to Weights-072--1.3934.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5006 - mean_absolute_error: 1.5006 - val_loss: 1.3934 - val_mean_absolute_error: 1.3934\n",
            "Epoch 73/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.2638 - mean_absolute_error: 2.2638\n",
            "Epoch 00073: val_loss did not improve from 1.39336\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5000 - mean_absolute_error: 1.5000 - val_loss: 1.3986 - val_mean_absolute_error: 1.3986\n",
            "Epoch 74/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4982 - mean_absolute_error: 1.4982\n",
            "Epoch 00074: val_loss did not improve from 1.39336\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.5045 - mean_absolute_error: 1.5045 - val_loss: 1.3995 - val_mean_absolute_error: 1.3995\n",
            "Epoch 75/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4900 - mean_absolute_error: 1.4900\n",
            "Epoch 00075: val_loss improved from 1.39336 to 1.39334, saving model to Weights-075--1.3933.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.5029 - mean_absolute_error: 1.5029 - val_loss: 1.3933 - val_mean_absolute_error: 1.3933\n",
            "Epoch 76/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8922 - mean_absolute_error: 0.8922\n",
            "Epoch 00076: val_loss improved from 1.39334 to 1.39232, saving model to Weights-076--1.3923.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.5008 - mean_absolute_error: 1.5008 - val_loss: 1.3923 - val_mean_absolute_error: 1.3923\n",
            "Epoch 77/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4246 - mean_absolute_error: 1.4246\n",
            "Epoch 00077: val_loss did not improve from 1.39232\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4981 - mean_absolute_error: 1.4981 - val_loss: 1.3936 - val_mean_absolute_error: 1.3936\n",
            "Epoch 78/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3198 - mean_absolute_error: 1.3198\n",
            "Epoch 00078: val_loss did not improve from 1.39232\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.4990 - mean_absolute_error: 1.4990 - val_loss: 1.3954 - val_mean_absolute_error: 1.3954\n",
            "Epoch 79/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.4935 - mean_absolute_error: 2.4935\n",
            "Epoch 00079: val_loss improved from 1.39232 to 1.39003, saving model to Weights-079--1.3900.hdf5\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 1.4967 - mean_absolute_error: 1.4967 - val_loss: 1.3900 - val_mean_absolute_error: 1.3900\n",
            "Epoch 80/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5226 - mean_absolute_error: 1.5226\n",
            "Epoch 00080: val_loss did not improve from 1.39003\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.4982 - mean_absolute_error: 1.4982 - val_loss: 1.3921 - val_mean_absolute_error: 1.3921\n",
            "Epoch 81/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2664 - mean_absolute_error: 1.2664\n",
            "Epoch 00081: val_loss did not improve from 1.39003\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4964 - mean_absolute_error: 1.4964 - val_loss: 1.3932 - val_mean_absolute_error: 1.3932\n",
            "Epoch 82/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8381 - mean_absolute_error: 1.8381\n",
            "Epoch 00082: val_loss improved from 1.39003 to 1.38818, saving model to Weights-082--1.3882.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.4957 - mean_absolute_error: 1.4957 - val_loss: 1.3882 - val_mean_absolute_error: 1.3882\n",
            "Epoch 83/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6439 - mean_absolute_error: 1.6439\n",
            "Epoch 00083: val_loss improved from 1.38818 to 1.38750, saving model to Weights-083--1.3875.hdf5\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 1.4932 - mean_absolute_error: 1.4932 - val_loss: 1.3875 - val_mean_absolute_error: 1.3875\n",
            "Epoch 84/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5787 - mean_absolute_error: 1.5787\n",
            "Epoch 00084: val_loss did not improve from 1.38750\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4927 - mean_absolute_error: 1.4927 - val_loss: 1.3909 - val_mean_absolute_error: 1.3909\n",
            "Epoch 85/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0027 - mean_absolute_error: 1.0027\n",
            "Epoch 00085: val_loss did not improve from 1.38750\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4944 - mean_absolute_error: 1.4944 - val_loss: 1.3885 - val_mean_absolute_error: 1.3885\n",
            "Epoch 86/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.2456 - mean_absolute_error: 2.2456\n",
            "Epoch 00086: val_loss did not improve from 1.38750\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4930 - mean_absolute_error: 1.4930 - val_loss: 1.3877 - val_mean_absolute_error: 1.3877\n",
            "Epoch 87/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4445 - mean_absolute_error: 1.4445\n",
            "Epoch 00087: val_loss improved from 1.38750 to 1.38582, saving model to Weights-087--1.3858.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.4902 - mean_absolute_error: 1.4902 - val_loss: 1.3858 - val_mean_absolute_error: 1.3858\n",
            "Epoch 88/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8660 - mean_absolute_error: 0.8660\n",
            "Epoch 00088: val_loss did not improve from 1.38582\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.4918 - mean_absolute_error: 1.4918 - val_loss: 1.3860 - val_mean_absolute_error: 1.3860\n",
            "Epoch 89/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6698 - mean_absolute_error: 1.6698\n",
            "Epoch 00089: val_loss did not improve from 1.38582\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.4884 - mean_absolute_error: 1.4884 - val_loss: 1.3893 - val_mean_absolute_error: 1.3893\n",
            "Epoch 90/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5728 - mean_absolute_error: 1.5728\n",
            "Epoch 00090: val_loss did not improve from 1.38582\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4922 - mean_absolute_error: 1.4922 - val_loss: 1.3867 - val_mean_absolute_error: 1.3867\n",
            "Epoch 91/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6334 - mean_absolute_error: 0.6334\n",
            "Epoch 00091: val_loss improved from 1.38582 to 1.37919, saving model to Weights-091--1.3792.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.4870 - mean_absolute_error: 1.4870 - val_loss: 1.3792 - val_mean_absolute_error: 1.3792\n",
            "Epoch 92/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4825 - mean_absolute_error: 1.4825\n",
            "Epoch 00092: val_loss did not improve from 1.37919\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4893 - mean_absolute_error: 1.4893 - val_loss: 1.3800 - val_mean_absolute_error: 1.3800\n",
            "Epoch 93/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5539 - mean_absolute_error: 1.5539\n",
            "Epoch 00093: val_loss improved from 1.37919 to 1.37867, saving model to Weights-093--1.3787.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.4859 - mean_absolute_error: 1.4859 - val_loss: 1.3787 - val_mean_absolute_error: 1.3787\n",
            "Epoch 94/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9432 - mean_absolute_error: 0.9432\n",
            "Epoch 00094: val_loss improved from 1.37867 to 1.37814, saving model to Weights-094--1.3781.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.4874 - mean_absolute_error: 1.4874 - val_loss: 1.3781 - val_mean_absolute_error: 1.3781\n",
            "Epoch 95/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9415 - mean_absolute_error: 0.9415\n",
            "Epoch 00095: val_loss improved from 1.37814 to 1.37424, saving model to Weights-095--1.3742.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.4844 - mean_absolute_error: 1.4844 - val_loss: 1.3742 - val_mean_absolute_error: 1.3742\n",
            "Epoch 96/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7073 - mean_absolute_error: 0.7073\n",
            "Epoch 00096: val_loss did not improve from 1.37424\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4843 - mean_absolute_error: 1.4843 - val_loss: 1.3769 - val_mean_absolute_error: 1.3769\n",
            "Epoch 97/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.9217 - mean_absolute_error: 1.9217\n",
            "Epoch 00097: val_loss improved from 1.37424 to 1.37101, saving model to Weights-097--1.3710.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.4811 - mean_absolute_error: 1.4811 - val_loss: 1.3710 - val_mean_absolute_error: 1.3710\n",
            "Epoch 98/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.6526 - mean_absolute_error: 1.6526\n",
            "Epoch 00098: val_loss did not improve from 1.37101\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4834 - mean_absolute_error: 1.4834 - val_loss: 1.3718 - val_mean_absolute_error: 1.3718\n",
            "Epoch 99/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.8229 - mean_absolute_error: 1.8229\n",
            "Epoch 00099: val_loss did not improve from 1.37101\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 1.4811 - mean_absolute_error: 1.4811 - val_loss: 1.3774 - val_mean_absolute_error: 1.3774\n",
            "Epoch 100/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1242 - mean_absolute_error: 1.1242\n",
            "Epoch 00100: val_loss did not improve from 1.37101\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 1.4830 - mean_absolute_error: 1.4830 - val_loss: 1.3719 - val_mean_absolute_error: 1.3719\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7feca6a54450>"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "checkpoint = ModelCheckpoint('Weights-{epoch:03d}--{val_loss:.4f}.hdf5', monitor='val_loss', verbose = 1, save_best_only = True, mode ='auto')\n",
        "callbacks_list = [checkpoint]\n",
        "ANN_model.fit(X_train_std, Y_train_std, epochs=100, batch_size=32, validation_split = 0.2, callbacks=callbacks_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_VDEVEQ3DO6",
        "outputId": "00b4aa6f-d658-4c36-95bb-93954de2dc4b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Artificial Neural Network Mean Absolute Error:  1.3164015240647196\n",
            "R2 score using ANN:  0.053542491187745234\n"
          ]
        }
      ],
      "source": [
        "pred = ANN_model.predict(X_test_std)\n",
        "print(\"Artificial Neural Network Mean Absolute Error: \", mae(Y_test_std, pred))\n",
        "print(\"R2 score using ANN: \", r2_score(Y_test_std, pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "j_W9SrpmpzL6",
        "outputId": "5807192b-dd86-45a1-963d-51e15b740cbc"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Threads</th>\n",
              "      <th>Size</th>\n",
              "      <th>branch-instructions</th>\n",
              "      <th>branch-misses in %</th>\n",
              "      <th>L3-cache-misses</th>\n",
              "      <th>L3-cache-miss-rate in %</th>\n",
              "      <th>L3-cache-references</th>\n",
              "      <th>cpu-cycles</th>\n",
              "      <th>total-instructions</th>\n",
              "      <th>IPC</th>\n",
              "      <th>cpu-clock</th>\n",
              "      <th>page-faults</th>\n",
              "      <th>L1-data-cache-loads</th>\n",
              "      <th>L1-instruction-cache-load-misses</th>\n",
              "      <th>LLC-load-misses</th>\n",
              "      <th>Exe-time</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.901611</td>\n",
              "      <td>1.288039</td>\n",
              "      <td>-0.357983</td>\n",
              "      <td>-1.562463</td>\n",
              "      <td>-0.092164</td>\n",
              "      <td>1.881615</td>\n",
              "      <td>-0.112322</td>\n",
              "      <td>-0.126787</td>\n",
              "      <td>-0.148741</td>\n",
              "      <td>-0.374944</td>\n",
              "      <td>-0.127419</td>\n",
              "      <td>-0.660151</td>\n",
              "      <td>-0.116390</td>\n",
              "      <td>-0.922370</td>\n",
              "      <td>-0.091013</td>\n",
              "      <td>-0.068436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.635092</td>\n",
              "      <td>1.288039</td>\n",
              "      <td>-0.323165</td>\n",
              "      <td>-1.622742</td>\n",
              "      <td>-0.091920</td>\n",
              "      <td>1.897756</td>\n",
              "      <td>-0.112417</td>\n",
              "      <td>-0.132556</td>\n",
              "      <td>-0.145724</td>\n",
              "      <td>-0.167190</td>\n",
              "      <td>-0.132903</td>\n",
              "      <td>-0.657800</td>\n",
              "      <td>-0.120818</td>\n",
              "      <td>-0.923017</td>\n",
              "      <td>-0.090688</td>\n",
              "      <td>-0.122445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.102053</td>\n",
              "      <td>1.288039</td>\n",
              "      <td>-0.322253</td>\n",
              "      <td>-1.628770</td>\n",
              "      <td>-0.090791</td>\n",
              "      <td>1.938857</td>\n",
              "      <td>-0.112142</td>\n",
              "      <td>-0.130175</td>\n",
              "      <td>-0.145749</td>\n",
              "      <td>-0.229516</td>\n",
              "      <td>-0.130532</td>\n",
              "      <td>-0.664324</td>\n",
              "      <td>-0.120169</td>\n",
              "      <td>-0.923923</td>\n",
              "      <td>-0.089795</td>\n",
              "      <td>-0.146395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.964024</td>\n",
              "      <td>1.288039</td>\n",
              "      <td>-0.321150</td>\n",
              "      <td>-1.628770</td>\n",
              "      <td>-0.084538</td>\n",
              "      <td>2.244628</td>\n",
              "      <td>-0.112262</td>\n",
              "      <td>-0.128674</td>\n",
              "      <td>-0.144982</td>\n",
              "      <td>-0.271067</td>\n",
              "      <td>-0.129199</td>\n",
              "      <td>-0.664245</td>\n",
              "      <td>-0.118724</td>\n",
              "      <td>-0.923932</td>\n",
              "      <td>-0.083254</td>\n",
              "      <td>-0.158712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.096178</td>\n",
              "      <td>1.288039</td>\n",
              "      <td>-0.317926</td>\n",
              "      <td>-1.628770</td>\n",
              "      <td>-0.082416</td>\n",
              "      <td>2.344351</td>\n",
              "      <td>-0.112223</td>\n",
              "      <td>-0.128142</td>\n",
              "      <td>-0.144485</td>\n",
              "      <td>-0.291843</td>\n",
              "      <td>-0.128576</td>\n",
              "      <td>-0.664081</td>\n",
              "      <td>-0.118289</td>\n",
              "      <td>-0.922747</td>\n",
              "      <td>-0.081053</td>\n",
              "      <td>-0.166603</td>\n",
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            ],
            "text/plain": [
              "    Threads      Size  ...  LLC-load-misses  Exe-time\n",
              "0 -0.901611  1.288039  ...        -0.091013 -0.068436\n",
              "1 -0.635092  1.288039  ...        -0.090688 -0.122445\n",
              "2 -0.102053  1.288039  ...        -0.089795 -0.146395\n",
              "3  0.964024  1.288039  ...        -0.083254 -0.158712\n",
              "4  3.096178  1.288039  ...        -0.081053 -0.166603\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scaler_smogn = StandardScaler().fit(X_smogn)\n",
        "X_smogn_standardized = pd.DataFrame(scaler_smogn.transform(X_smogn),\n",
        "                              columns = X_smogn.columns)\n",
        "X_smogn_standardized.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "FZqVim5l1HAT"
      },
      "outputs": [],
      "source": [
        "X_train_smogn_std, X_test_smogn_std, Y_train_smogn_std, Y_test_smogn_std = train_test_split(X_smogn_standardized, Y_smogn, test_size=0.2, random_state=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "17MF5k7OK0pm"
      },
      "outputs": [],
      "source": [
        "ANN_smogn_model = Sequential()\n",
        "\n",
        "ANN_smogn_model.add(Dense(16, kernel_initializer='normal',input_dim = X_train_smogn_std.shape[1], activation='relu'))\n",
        "\n",
        "ANN_smogn_model.add(Dense(16, kernel_initializer='normal',activation='relu'))\n",
        "ANN_smogn_model.add(Dense(8, kernel_initializer='normal',activation='relu'))\n",
        "ANN_smogn_model.add(Dense(8, kernel_initializer='normal',activation='relu'))\n",
        "\n",
        "ANN_smogn_model.add(Dense(1, kernel_initializer='normal'))\n",
        "\n",
        "ANN_smogn_model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGDRwBYf2R7E",
        "outputId": "cba6feee-6b92-4b84-dedc-7cf24fadfe41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3111 - mean_absolute_error: 1.3111\n",
            "Epoch 00001: val_loss improved from inf to 0.97702, saving model to Weights-SMOGN-001--0.9770.hdf5\n",
            "7/7 [==============================] - 0s 24ms/step - loss: 1.1055 - mean_absolute_error: 1.1055 - val_loss: 0.9770 - val_mean_absolute_error: 0.9770\n",
            "Epoch 2/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6009 - mean_absolute_error: 0.6009\n",
            "Epoch 00002: val_loss improved from 0.97702 to 0.96382, saving model to Weights-SMOGN-002--0.9638.hdf5\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 1.0857 - mean_absolute_error: 1.0857 - val_loss: 0.9638 - val_mean_absolute_error: 0.9638\n",
            "Epoch 3/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1599 - mean_absolute_error: 1.1599\n",
            "Epoch 00003: val_loss improved from 0.96382 to 0.95568, saving model to Weights-SMOGN-003--0.9557.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.0632 - mean_absolute_error: 1.0632 - val_loss: 0.9557 - val_mean_absolute_error: 0.9557\n",
            "Epoch 4/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2985 - mean_absolute_error: 1.2985\n",
            "Epoch 00004: val_loss improved from 0.95568 to 0.95144, saving model to Weights-SMOGN-004--0.9514.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 1.0424 - mean_absolute_error: 1.0424 - val_loss: 0.9514 - val_mean_absolute_error: 0.9514\n",
            "Epoch 5/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1943 - mean_absolute_error: 1.1943\n",
            "Epoch 00005: val_loss improved from 0.95144 to 0.94332, saving model to Weights-SMOGN-005--0.9433.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 1.0145 - mean_absolute_error: 1.0145 - val_loss: 0.9433 - val_mean_absolute_error: 0.9433\n",
            "Epoch 6/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3632 - mean_absolute_error: 1.3632\n",
            "Epoch 00006: val_loss improved from 0.94332 to 0.93594, saving model to Weights-SMOGN-006--0.9359.hdf5\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.9950 - mean_absolute_error: 0.9950 - val_loss: 0.9359 - val_mean_absolute_error: 0.9359\n",
            "Epoch 7/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9710 - mean_absolute_error: 0.9710\n",
            "Epoch 00007: val_loss improved from 0.93594 to 0.93572, saving model to Weights-SMOGN-007--0.9357.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.9825 - mean_absolute_error: 0.9825 - val_loss: 0.9357 - val_mean_absolute_error: 0.9357\n",
            "Epoch 8/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0146 - mean_absolute_error: 1.0146\n",
            "Epoch 00008: val_loss improved from 0.93572 to 0.93430, saving model to Weights-SMOGN-008--0.9343.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.9742 - mean_absolute_error: 0.9742 - val_loss: 0.9343 - val_mean_absolute_error: 0.9343\n",
            "Epoch 9/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.0035 - mean_absolute_error: 2.0035\n",
            "Epoch 00009: val_loss did not improve from 0.93430\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 0.9681 - mean_absolute_error: 0.9681 - val_loss: 0.9376 - val_mean_absolute_error: 0.9376\n",
            "Epoch 10/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7914 - mean_absolute_error: 0.7914\n",
            "Epoch 00010: val_loss did not improve from 0.93430\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9644 - mean_absolute_error: 0.9644 - val_loss: 0.9377 - val_mean_absolute_error: 0.9377\n",
            "Epoch 11/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4416 - mean_absolute_error: 0.4416\n",
            "Epoch 00011: val_loss did not improve from 0.93430\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9610 - mean_absolute_error: 0.9610 - val_loss: 0.9370 - val_mean_absolute_error: 0.9370\n",
            "Epoch 12/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2176 - mean_absolute_error: 1.2176\n",
            "Epoch 00012: val_loss improved from 0.93430 to 0.93299, saving model to Weights-SMOGN-012--0.9330.hdf5\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.9585 - mean_absolute_error: 0.9585 - val_loss: 0.9330 - val_mean_absolute_error: 0.9330\n",
            "Epoch 13/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9418 - mean_absolute_error: 0.9418\n",
            "Epoch 00013: val_loss did not improve from 0.93299\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9577 - mean_absolute_error: 0.9577 - val_loss: 0.9333 - val_mean_absolute_error: 0.9333\n",
            "Epoch 14/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9647 - mean_absolute_error: 0.9647\n",
            "Epoch 00014: val_loss did not improve from 0.93299\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9536 - mean_absolute_error: 0.9536 - val_loss: 0.9365 - val_mean_absolute_error: 0.9365\n",
            "Epoch 15/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4568 - mean_absolute_error: 0.4568\n",
            "Epoch 00015: val_loss improved from 0.93299 to 0.93275, saving model to Weights-SMOGN-015--0.9327.hdf5\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.9503 - mean_absolute_error: 0.9503 - val_loss: 0.9327 - val_mean_absolute_error: 0.9327\n",
            "Epoch 16/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6196 - mean_absolute_error: 0.6196\n",
            "Epoch 00016: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9469 - mean_absolute_error: 0.9469 - val_loss: 0.9349 - val_mean_absolute_error: 0.9349\n",
            "Epoch 17/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8661 - mean_absolute_error: 0.8661\n",
            "Epoch 00017: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9417 - mean_absolute_error: 0.9417 - val_loss: 0.9382 - val_mean_absolute_error: 0.9382\n",
            "Epoch 18/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9735 - mean_absolute_error: 0.9735\n",
            "Epoch 00018: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.9388 - mean_absolute_error: 0.9388 - val_loss: 0.9393 - val_mean_absolute_error: 0.9393\n",
            "Epoch 19/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4761 - mean_absolute_error: 0.4761\n",
            "Epoch 00019: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9353 - mean_absolute_error: 0.9353 - val_loss: 0.9413 - val_mean_absolute_error: 0.9413\n",
            "Epoch 20/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9999 - mean_absolute_error: 0.9999\n",
            "Epoch 00020: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9342 - mean_absolute_error: 0.9342 - val_loss: 0.9439 - val_mean_absolute_error: 0.9439\n",
            "Epoch 21/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0026 - mean_absolute_error: 1.0026\n",
            "Epoch 00021: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9296 - mean_absolute_error: 0.9296 - val_loss: 0.9411 - val_mean_absolute_error: 0.9411\n",
            "Epoch 22/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9682 - mean_absolute_error: 0.9682\n",
            "Epoch 00022: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9303 - mean_absolute_error: 0.9303 - val_loss: 0.9448 - val_mean_absolute_error: 0.9448\n",
            "Epoch 23/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3418 - mean_absolute_error: 1.3418\n",
            "Epoch 00023: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9255 - mean_absolute_error: 0.9255 - val_loss: 0.9483 - val_mean_absolute_error: 0.9483\n",
            "Epoch 24/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0929 - mean_absolute_error: 1.0929\n",
            "Epoch 00024: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9235 - mean_absolute_error: 0.9235 - val_loss: 0.9492 - val_mean_absolute_error: 0.9492\n",
            "Epoch 25/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2492 - mean_absolute_error: 1.2492\n",
            "Epoch 00025: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9187 - mean_absolute_error: 0.9187 - val_loss: 0.9496 - val_mean_absolute_error: 0.9496\n",
            "Epoch 26/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0000 - mean_absolute_error: 1.0000\n",
            "Epoch 00026: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9142 - mean_absolute_error: 0.9142 - val_loss: 0.9489 - val_mean_absolute_error: 0.9489\n",
            "Epoch 27/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5918 - mean_absolute_error: 0.5918\n",
            "Epoch 00027: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9132 - mean_absolute_error: 0.9132 - val_loss: 0.9487 - val_mean_absolute_error: 0.9487\n",
            "Epoch 28/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0356 - mean_absolute_error: 1.0356\n",
            "Epoch 00028: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9093 - mean_absolute_error: 0.9093 - val_loss: 0.9549 - val_mean_absolute_error: 0.9549\n",
            "Epoch 29/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8546 - mean_absolute_error: 0.8546\n",
            "Epoch 00029: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.9093 - mean_absolute_error: 0.9093 - val_loss: 0.9553 - val_mean_absolute_error: 0.9553\n",
            "Epoch 30/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1072 - mean_absolute_error: 1.1072\n",
            "Epoch 00030: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9043 - mean_absolute_error: 0.9043 - val_loss: 0.9553 - val_mean_absolute_error: 0.9553\n",
            "Epoch 31/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7449 - mean_absolute_error: 0.7449\n",
            "Epoch 00031: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.9010 - mean_absolute_error: 0.9010 - val_loss: 0.9582 - val_mean_absolute_error: 0.9582\n",
            "Epoch 32/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3416 - mean_absolute_error: 1.3416\n",
            "Epoch 00032: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8997 - mean_absolute_error: 0.8997 - val_loss: 0.9593 - val_mean_absolute_error: 0.9593\n",
            "Epoch 33/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7957 - mean_absolute_error: 0.7957\n",
            "Epoch 00033: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 0.8972 - mean_absolute_error: 0.8972 - val_loss: 0.9615 - val_mean_absolute_error: 0.9615\n",
            "Epoch 34/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0648 - mean_absolute_error: 1.0648\n",
            "Epoch 00034: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8982 - mean_absolute_error: 0.8982 - val_loss: 0.9612 - val_mean_absolute_error: 0.9612\n",
            "Epoch 35/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4470 - mean_absolute_error: 1.4470\n",
            "Epoch 00035: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8959 - mean_absolute_error: 0.8959 - val_loss: 0.9661 - val_mean_absolute_error: 0.9661\n",
            "Epoch 36/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.3997 - mean_absolute_error: 0.3997\n",
            "Epoch 00036: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8925 - mean_absolute_error: 0.8925 - val_loss: 0.9674 - val_mean_absolute_error: 0.9674\n",
            "Epoch 37/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7549 - mean_absolute_error: 0.7549\n",
            "Epoch 00037: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.8946 - mean_absolute_error: 0.8946 - val_loss: 0.9668 - val_mean_absolute_error: 0.9668\n",
            "Epoch 38/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6689 - mean_absolute_error: 0.6689\n",
            "Epoch 00038: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8910 - mean_absolute_error: 0.8910 - val_loss: 0.9651 - val_mean_absolute_error: 0.9651\n",
            "Epoch 39/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.2859 - mean_absolute_error: 0.2859\n",
            "Epoch 00039: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8912 - mean_absolute_error: 0.8912 - val_loss: 0.9671 - val_mean_absolute_error: 0.9671\n",
            "Epoch 40/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9858 - mean_absolute_error: 0.9858\n",
            "Epoch 00040: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8887 - mean_absolute_error: 0.8887 - val_loss: 0.9703 - val_mean_absolute_error: 0.9703\n",
            "Epoch 41/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8944 - mean_absolute_error: 0.8944\n",
            "Epoch 00041: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8879 - mean_absolute_error: 0.8879 - val_loss: 0.9693 - val_mean_absolute_error: 0.9693\n",
            "Epoch 42/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6510 - mean_absolute_error: 0.6510\n",
            "Epoch 00042: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8856 - mean_absolute_error: 0.8856 - val_loss: 0.9711 - val_mean_absolute_error: 0.9711\n",
            "Epoch 43/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3962 - mean_absolute_error: 1.3962\n",
            "Epoch 00043: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8837 - mean_absolute_error: 0.8837 - val_loss: 0.9713 - val_mean_absolute_error: 0.9713\n",
            "Epoch 44/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5948 - mean_absolute_error: 0.5948\n",
            "Epoch 00044: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 0.8817 - mean_absolute_error: 0.8817 - val_loss: 0.9679 - val_mean_absolute_error: 0.9679\n",
            "Epoch 45/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8505 - mean_absolute_error: 0.8505\n",
            "Epoch 00045: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8827 - mean_absolute_error: 0.8827 - val_loss: 0.9647 - val_mean_absolute_error: 0.9647\n",
            "Epoch 46/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7114 - mean_absolute_error: 0.7114\n",
            "Epoch 00046: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8848 - mean_absolute_error: 0.8848 - val_loss: 0.9685 - val_mean_absolute_error: 0.9685\n",
            "Epoch 47/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5260 - mean_absolute_error: 0.5260\n",
            "Epoch 00047: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8849 - mean_absolute_error: 0.8849 - val_loss: 0.9717 - val_mean_absolute_error: 0.9717\n",
            "Epoch 48/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4639 - mean_absolute_error: 0.4639\n",
            "Epoch 00048: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8790 - mean_absolute_error: 0.8790 - val_loss: 0.9767 - val_mean_absolute_error: 0.9767\n",
            "Epoch 49/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6784 - mean_absolute_error: 0.6784\n",
            "Epoch 00049: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8796 - mean_absolute_error: 0.8796 - val_loss: 0.9785 - val_mean_absolute_error: 0.9785\n",
            "Epoch 50/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2139 - mean_absolute_error: 1.2139\n",
            "Epoch 00050: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8797 - mean_absolute_error: 0.8797 - val_loss: 0.9837 - val_mean_absolute_error: 0.9837\n",
            "Epoch 51/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9648 - mean_absolute_error: 0.9648\n",
            "Epoch 00051: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8754 - mean_absolute_error: 0.8754 - val_loss: 0.9845 - val_mean_absolute_error: 0.9845\n",
            "Epoch 52/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6671 - mean_absolute_error: 0.6671\n",
            "Epoch 00052: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.8747 - mean_absolute_error: 0.8747 - val_loss: 0.9902 - val_mean_absolute_error: 0.9902\n",
            "Epoch 53/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4840 - mean_absolute_error: 1.4840\n",
            "Epoch 00053: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8731 - mean_absolute_error: 0.8731 - val_loss: 0.9943 - val_mean_absolute_error: 0.9943\n",
            "Epoch 54/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8585 - mean_absolute_error: 0.8585\n",
            "Epoch 00054: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8704 - mean_absolute_error: 0.8704 - val_loss: 0.9956 - val_mean_absolute_error: 0.9956\n",
            "Epoch 55/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5756 - mean_absolute_error: 0.5756\n",
            "Epoch 00055: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8695 - mean_absolute_error: 0.8695 - val_loss: 0.9904 - val_mean_absolute_error: 0.9904\n",
            "Epoch 56/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2062 - mean_absolute_error: 1.2062\n",
            "Epoch 00056: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8690 - mean_absolute_error: 0.8690 - val_loss: 0.9868 - val_mean_absolute_error: 0.9868\n",
            "Epoch 57/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8060 - mean_absolute_error: 0.8060\n",
            "Epoch 00057: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8648 - mean_absolute_error: 0.8648 - val_loss: 0.9908 - val_mean_absolute_error: 0.9908\n",
            "Epoch 58/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1939 - mean_absolute_error: 1.1939\n",
            "Epoch 00058: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8689 - mean_absolute_error: 0.8689 - val_loss: 0.9943 - val_mean_absolute_error: 0.9943\n",
            "Epoch 59/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4250 - mean_absolute_error: 0.4250\n",
            "Epoch 00059: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8633 - mean_absolute_error: 0.8633 - val_loss: 0.9967 - val_mean_absolute_error: 0.9967\n",
            "Epoch 60/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0351 - mean_absolute_error: 1.0351\n",
            "Epoch 00060: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 0.8665 - mean_absolute_error: 0.8665 - val_loss: 0.9972 - val_mean_absolute_error: 0.9972\n",
            "Epoch 61/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5287 - mean_absolute_error: 0.5287\n",
            "Epoch 00061: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8588 - mean_absolute_error: 0.8588 - val_loss: 0.9939 - val_mean_absolute_error: 0.9939\n",
            "Epoch 62/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4999 - mean_absolute_error: 0.4999\n",
            "Epoch 00062: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8603 - mean_absolute_error: 0.8603 - val_loss: 0.9894 - val_mean_absolute_error: 0.9894\n",
            "Epoch 63/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7840 - mean_absolute_error: 0.7840\n",
            "Epoch 00063: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8575 - mean_absolute_error: 0.8575 - val_loss: 0.9915 - val_mean_absolute_error: 0.9915\n",
            "Epoch 64/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8901 - mean_absolute_error: 0.8901\n",
            "Epoch 00064: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8566 - mean_absolute_error: 0.8566 - val_loss: 0.9919 - val_mean_absolute_error: 0.9919\n",
            "Epoch 65/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0829 - mean_absolute_error: 1.0829\n",
            "Epoch 00065: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8563 - mean_absolute_error: 0.8563 - val_loss: 0.9951 - val_mean_absolute_error: 0.9951\n",
            "Epoch 66/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8319 - mean_absolute_error: 0.8319\n",
            "Epoch 00066: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8549 - mean_absolute_error: 0.8549 - val_loss: 0.9999 - val_mean_absolute_error: 0.9999\n",
            "Epoch 67/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.2304 - mean_absolute_error: 0.2304\n",
            "Epoch 00067: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8556 - mean_absolute_error: 0.8556 - val_loss: 0.9973 - val_mean_absolute_error: 0.9973\n",
            "Epoch 68/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8380 - mean_absolute_error: 0.8380\n",
            "Epoch 00068: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8596 - mean_absolute_error: 0.8596 - val_loss: 1.0065 - val_mean_absolute_error: 1.0065\n",
            "Epoch 69/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7250 - mean_absolute_error: 0.7250\n",
            "Epoch 00069: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8524 - mean_absolute_error: 0.8524 - val_loss: 1.0049 - val_mean_absolute_error: 1.0049\n",
            "Epoch 70/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.3125 - mean_absolute_error: 1.3125\n",
            "Epoch 00070: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8530 - mean_absolute_error: 0.8530 - val_loss: 1.0063 - val_mean_absolute_error: 1.0063\n",
            "Epoch 71/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5886 - mean_absolute_error: 0.5886\n",
            "Epoch 00071: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8511 - mean_absolute_error: 0.8511 - val_loss: 1.0050 - val_mean_absolute_error: 1.0050\n",
            "Epoch 72/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 2.1273 - mean_absolute_error: 2.1273\n",
            "Epoch 00072: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8475 - mean_absolute_error: 0.8475 - val_loss: 1.0070 - val_mean_absolute_error: 1.0070\n",
            "Epoch 73/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9386 - mean_absolute_error: 0.9386\n",
            "Epoch 00073: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8506 - mean_absolute_error: 0.8506 - val_loss: 1.0112 - val_mean_absolute_error: 1.0112\n",
            "Epoch 74/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0529 - mean_absolute_error: 1.0529\n",
            "Epoch 00074: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8440 - mean_absolute_error: 0.8440 - val_loss: 1.0124 - val_mean_absolute_error: 1.0124\n",
            "Epoch 75/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.3957 - mean_absolute_error: 0.3957\n",
            "Epoch 00075: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8415 - mean_absolute_error: 0.8415 - val_loss: 1.0131 - val_mean_absolute_error: 1.0131\n",
            "Epoch 76/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9839 - mean_absolute_error: 0.9839\n",
            "Epoch 00076: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8418 - mean_absolute_error: 0.8418 - val_loss: 1.0140 - val_mean_absolute_error: 1.0140\n",
            "Epoch 77/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1926 - mean_absolute_error: 1.1926\n",
            "Epoch 00077: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8446 - mean_absolute_error: 0.8446 - val_loss: 1.0202 - val_mean_absolute_error: 1.0202\n",
            "Epoch 78/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6004 - mean_absolute_error: 0.6004\n",
            "Epoch 00078: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8409 - mean_absolute_error: 0.8409 - val_loss: 1.0180 - val_mean_absolute_error: 1.0180\n",
            "Epoch 79/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.5447 - mean_absolute_error: 1.5447\n",
            "Epoch 00079: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8398 - mean_absolute_error: 0.8398 - val_loss: 1.0219 - val_mean_absolute_error: 1.0219\n",
            "Epoch 80/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.2553 - mean_absolute_error: 1.2553\n",
            "Epoch 00080: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8372 - mean_absolute_error: 0.8372 - val_loss: 1.0247 - val_mean_absolute_error: 1.0247\n",
            "Epoch 81/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7779 - mean_absolute_error: 0.7779\n",
            "Epoch 00081: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8327 - mean_absolute_error: 0.8327 - val_loss: 1.0329 - val_mean_absolute_error: 1.0329\n",
            "Epoch 82/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1367 - mean_absolute_error: 1.1367\n",
            "Epoch 00082: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8301 - mean_absolute_error: 0.8301 - val_loss: 1.0397 - val_mean_absolute_error: 1.0397\n",
            "Epoch 83/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.9264 - mean_absolute_error: 0.9264\n",
            "Epoch 00083: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8288 - mean_absolute_error: 0.8288 - val_loss: 1.0373 - val_mean_absolute_error: 1.0373\n",
            "Epoch 84/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1093 - mean_absolute_error: 1.1093\n",
            "Epoch 00084: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8271 - mean_absolute_error: 0.8271 - val_loss: 1.0384 - val_mean_absolute_error: 1.0384\n",
            "Epoch 85/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.4223 - mean_absolute_error: 1.4223\n",
            "Epoch 00085: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8319 - mean_absolute_error: 0.8319 - val_loss: 1.0458 - val_mean_absolute_error: 1.0458\n",
            "Epoch 86/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.4990 - mean_absolute_error: 0.4990\n",
            "Epoch 00086: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8296 - mean_absolute_error: 0.8296 - val_loss: 1.0431 - val_mean_absolute_error: 1.0431\n",
            "Epoch 87/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.6727 - mean_absolute_error: 0.6727\n",
            "Epoch 00087: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8236 - mean_absolute_error: 0.8236 - val_loss: 1.0469 - val_mean_absolute_error: 1.0469\n",
            "Epoch 88/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.7839 - mean_absolute_error: 1.7839\n",
            "Epoch 00088: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.8290 - mean_absolute_error: 0.8290 - val_loss: 1.0464 - val_mean_absolute_error: 1.0464\n",
            "Epoch 89/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.2358 - mean_absolute_error: 0.2358\n",
            "Epoch 00089: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8283 - mean_absolute_error: 0.8283 - val_loss: 1.0410 - val_mean_absolute_error: 1.0410\n",
            "Epoch 90/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7796 - mean_absolute_error: 0.7796\n",
            "Epoch 00090: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8241 - mean_absolute_error: 0.8241 - val_loss: 1.0395 - val_mean_absolute_error: 1.0395\n",
            "Epoch 91/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8038 - mean_absolute_error: 0.8038\n",
            "Epoch 00091: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8201 - mean_absolute_error: 0.8201 - val_loss: 1.0321 - val_mean_absolute_error: 1.0321\n",
            "Epoch 92/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.2851 - mean_absolute_error: 0.2851\n",
            "Epoch 00092: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8191 - mean_absolute_error: 0.8191 - val_loss: 1.0377 - val_mean_absolute_error: 1.0377\n",
            "Epoch 93/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.7204 - mean_absolute_error: 0.7204\n",
            "Epoch 00093: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8169 - mean_absolute_error: 0.8169 - val_loss: 1.0346 - val_mean_absolute_error: 1.0346\n",
            "Epoch 94/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0051 - mean_absolute_error: 1.0051\n",
            "Epoch 00094: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8160 - mean_absolute_error: 0.8160 - val_loss: 1.0391 - val_mean_absolute_error: 1.0391\n",
            "Epoch 95/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.1554 - mean_absolute_error: 1.1554\n",
            "Epoch 00095: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8127 - mean_absolute_error: 0.8127 - val_loss: 1.0420 - val_mean_absolute_error: 1.0420\n",
            "Epoch 96/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5990 - mean_absolute_error: 0.5990\n",
            "Epoch 00096: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8116 - mean_absolute_error: 0.8116 - val_loss: 1.0462 - val_mean_absolute_error: 1.0462\n",
            "Epoch 97/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 1.0745 - mean_absolute_error: 1.0745\n",
            "Epoch 00097: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 0.8104 - mean_absolute_error: 0.8104 - val_loss: 1.0537 - val_mean_absolute_error: 1.0537\n",
            "Epoch 98/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.5901 - mean_absolute_error: 0.5901\n",
            "Epoch 00098: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.8117 - mean_absolute_error: 0.8117 - val_loss: 1.0624 - val_mean_absolute_error: 1.0624\n",
            "Epoch 99/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8455 - mean_absolute_error: 0.8455\n",
            "Epoch 00099: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8087 - mean_absolute_error: 0.8087 - val_loss: 1.0631 - val_mean_absolute_error: 1.0631\n",
            "Epoch 100/100\n",
            "1/7 [===>..........................] - ETA: 0s - loss: 0.8688 - mean_absolute_error: 0.8688\n",
            "Epoch 00100: val_loss did not improve from 0.93275\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.8075 - mean_absolute_error: 0.8075 - val_loss: 1.0630 - val_mean_absolute_error: 1.0630\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7feca1b607d0>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "checkpoint_smogn = ModelCheckpoint('Weights-SMOGN-{epoch:03d}--{val_loss:.4f}.hdf5', monitor='val_loss', verbose = 1, save_best_only = True, mode ='auto')\n",
        "callbacks_list_smogn = [checkpoint_smogn]\n",
        "ANN_model.fit(X_train_smogn_std, Y_train_smogn_std, epochs=100, batch_size=32, validation_split = 0.2, callbacks=callbacks_list_smogn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FL4NoIEeMSgW",
        "outputId": "dd2bf960-d69f-4646-f6b6-bfab7af0e2c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Artificial Neural Network SMOGN Mean Absolute Error:  1.5641698153488666\n",
            "R2 score using SMOGN ANN:  -0.6088918604920808\n"
          ]
        }
      ],
      "source": [
        "pred_smogn = ANN_smogn_model.predict(X_test_smogn_std)\n",
        "print(\"Artificial Neural Network SMOGN Mean Absolute Error: \", mae(Y_test_smogn_std, pred_smogn))\n",
        "print(\"R2 score using SMOGN ANN: \", r2_score(Y_test_smogn_std, pred_smogn))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "performance_prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}